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    "input": "What correction should a teacher give for 3(x+2)=18 becoming 3x+2=18?",
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    "fixture_id": "cycle-01-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
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    "number": 22,
    "fixture_id": "cycle-01-doc-teacher_feedback",
    "utc": "2026-09-29T15:54:48.305769+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 23,
    "fixture_id": "cycle-01-doc-library_access",
    "utc": "2026-09-29T15:54:49.285892+00:00",
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    "number": 25,
    "fixture_id": "cycle-01-doc-attendance_review",
    "utc": "2026-09-29T15:54:51.065429+00:00",
    "status": 200,
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 28,
    "fixture_id": "cycle-01-doc-document_ingestion",
    "utc": "2026-09-29T15:54:53.958380+00:00",
    "status": 200,
    "latency_seconds": 0.992332780995639,
    "dimensions": 1024,
    "norm": 1.000000321965477,
    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
      "prompt_tokens": 411,
      "total_tokens": 411
    },
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  {
    "condition": "A",
    "number": 29,
    "fixture_id": "cycle-01-doc-service_health",
    "utc": "2026-09-29T15:54:55.018965+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "fixture_id": "cycle-02-doc-fraction_number_line",
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    "status": 200,
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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  {
    "condition": "A",
    "number": 39,
    "fixture_id": "cycle-02-doc-reading_pilot",
    "utc": "2026-09-29T15:54:59.240080+00:00",
    "status": 200,
    "latency_seconds": 1.2593418469768949,
    "dimensions": 1024,
    "norm": 0.9999999369619573,
    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "number": 41,
    "fixture_id": "cycle-02-doc-teacher_feedback",
    "utc": "2026-09-29T15:55:01.252848+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 42,
    "fixture_id": "cycle-02-doc-library_access",
    "utc": "2026-09-29T15:55:02.263990+00:00",
    "status": 200,
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    "input": "Section 1 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 2 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 3 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 4 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 5 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.",
    "input_sha256": "5b7b31badfc945853e8296212d913af0a0308c7ab52ebe9667b0b57e01655741",
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    "fixture_id": "cycle-02-doc-attendance_review",
    "utc": "2026-09-29T15:55:04.211146+00:00",
    "status": 200,
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 47,
    "fixture_id": "cycle-02-doc-document_ingestion",
    "utc": "2026-09-29T15:55:06.994765+00:00",
    "status": 200,
    "latency_seconds": 0.8343152389861643,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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      "total_tokens": 411
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  {
    "condition": "A",
    "number": 48,
    "fixture_id": "cycle-02-doc-service_health",
    "utc": "2026-09-29T15:55:08.018028+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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      "total_tokens": 471
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    "fixture_id": "cycle-03-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "A",
    "number": 58,
    "fixture_id": "cycle-03-doc-reading_pilot",
    "utc": "2026-09-29T15:55:12.546540+00:00",
    "status": 200,
    "latency_seconds": 1.225787475996185,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "number": 60,
    "fixture_id": "cycle-03-doc-teacher_feedback",
    "utc": "2026-09-29T15:55:14.544874+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 61,
    "fixture_id": "cycle-03-doc-library_access",
    "utc": "2026-09-29T15:55:15.510852+00:00",
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    "condition": "A",
    "number": 63,
    "fixture_id": "cycle-03-doc-attendance_review",
    "utc": "2026-09-29T15:55:17.355973+00:00",
    "status": 200,
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
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  {
    "condition": "A",
    "number": 67,
    "fixture_id": "cycle-03-doc-service_health",
    "utc": "2026-09-29T15:55:21.336179+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "fixture_id": "cycle-04-doc-fraction_number_line",
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    "input": "Section 1 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 2 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 3 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 4 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 5 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "A",
    "number": 77,
    "fixture_id": "cycle-04-doc-reading_pilot",
    "utc": "2026-09-29T15:55:25.704881+00:00",
    "status": 200,
    "latency_seconds": 1.1364472400164232,
    "dimensions": 1024,
    "norm": 0.9999999369619573,
    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
    "usage": {
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    "input": "Section 1 of After-school math tutoring. The fictional Cedar School math tutoring program serves grades six through eight. It meets Tuesday and Thursday from 3:30 to 4:15 in Room 204. A late bus is available on Thursday only. Students bring a notebook. The announcement contains no student fee information. Staff answer schedule questions from the written notice and say clearly when a requested detail is absent instead of inventing it.\n\nSection 2 of After-school math tutoring. The fictional Cedar School math tutoring program serves grades six through eight. It meets Tuesday and Thursday from 3:30 to 4:15 in Room 204. A late bus is available on Thursday only. Students bring a notebook. The announcement contains no student fee information. Staff answer schedule questions from the written notice and say clearly when a requested detail is absent instead of inventing it.\n\nSection 3 of After-school math tutoring. The fictional Cedar School math tutoring program serves grades six through eight. It meets Tuesday and Thursday from 3:30 to 4:15 in Room 204. A late bus is available on Thursday only. Students bring a notebook. The announcement contains no student fee information. Staff answer schedule questions from the written notice and say clearly when a requested detail is absent instead of inventing it.\n\nSection 4 of After-school math tutoring. The fictional Cedar School math tutoring program serves grades six through eight. It meets Tuesday and Thursday from 3:30 to 4:15 in Room 204. A late bus is available on Thursday only. Students bring a notebook. The announcement contains no student fee information. Staff answer schedule questions from the written notice and say clearly when a requested detail is absent instead of inventing it.\n\nSection 5 of After-school math tutoring. The fictional Cedar School math tutoring program serves grades six through eight. It meets Tuesday and Thursday from 3:30 to 4:15 in Room 204. A late bus is available on Thursday only. Students bring a notebook. The announcement contains no student fee information. Staff answer schedule questions from the written notice and say clearly when a requested detail is absent instead of inventing it.",
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    "number": 79,
    "fixture_id": "cycle-04-doc-teacher_feedback",
    "utc": "2026-09-29T15:55:27.635634+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 80,
    "fixture_id": "cycle-04-doc-library_access",
    "utc": "2026-09-29T15:55:28.573965+00:00",
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    "fixture_id": "cycle-04-doc-attendance_review",
    "utc": "2026-09-29T15:55:30.339117+00:00",
    "status": 200,
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    "number": 83,
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 85,
    "fixture_id": "cycle-04-doc-document_ingestion",
    "utc": "2026-09-29T15:55:33.344325+00:00",
    "status": 200,
    "latency_seconds": 1.0080648779985495,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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      "total_tokens": 411
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  {
    "condition": "A",
    "number": 86,
    "fixture_id": "cycle-04-doc-service_health",
    "utc": "2026-09-29T15:55:34.357303+00:00",
    "status": 200,
    "latency_seconds": 1.0125821900146548,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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      "total_tokens": 471
    },
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    "fixture_id": "cycle-05-doc-fraction_number_line",
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    "fixture_id": "cycle-05-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "A",
    "number": 96,
    "fixture_id": "cycle-05-doc-reading_pilot",
    "utc": "2026-09-29T15:55:38.668575+00:00",
    "status": 200,
    "latency_seconds": 1.1733639400044922,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "number": 98,
    "fixture_id": "cycle-05-doc-teacher_feedback",
    "utc": "2026-09-29T15:55:40.693527+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
    "input_sha256": "62daf22f778094831c222f489bda61334686ab58335ab53694f01d7e34934fab",
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    "number": 99,
    "fixture_id": "cycle-05-doc-library_access",
    "utc": "2026-09-29T15:55:41.755970+00:00",
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    "input": "Section 1 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 2 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 3 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 4 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.\n\nSection 5 of Science fair notice. The fictional Cedar School science fair will take place on November 19, 2026, in Room 317. This notice explains where students should display projects and asks volunteers to arrive early for setup. It does not state whether an admission fee is charged. When answering a family question, staff should provide the date and room and state that the fee is not specified in this notice.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 104,
    "fixture_id": "cycle-05-doc-document_ingestion",
    "utc": "2026-09-29T15:55:46.318942+00:00",
    "status": 200,
    "latency_seconds": 0.9005142920068465,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
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  {
    "condition": "A",
    "number": 105,
    "fixture_id": "cycle-05-doc-service_health",
    "utc": "2026-09-29T15:55:47.288981+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 2 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 3 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 4 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.\n\nSection 5 of Grade 3 fractions on a number line. A grade 3 fraction lesson uses a number line from zero to one. Students partition the unit interval into equal parts, count lengths of one over the denominator, and locate a fraction at its endpoint. The teacher asks learners to explain why one eighth lies to the left of one sixth. A quick exit ticket asks students to mark three fourths and defend the placement with equal intervals. The standard focus is representing fractions as numbers on a number line.",
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    "fixture_id": "cycle-06-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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  {
    "condition": "A",
    "number": 115,
    "fixture_id": "cycle-06-doc-reading_pilot",
    "utc": "2026-09-29T15:55:52.582332+00:00",
    "status": 200,
    "latency_seconds": 1.2214026319852564,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
    "usage": {
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    "fixture_id": "cycle-06-doc-teacher_feedback",
    "utc": "2026-09-29T15:55:54.630801+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "fixture_id": "cycle-06-doc-library_access",
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    "number": 120,
    "fixture_id": "cycle-06-doc-attendance_review",
    "utc": "2026-09-29T15:55:57.401561+00:00",
    "status": 200,
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "A",
    "number": 121,
    "fixture_id": "cycle-06-doc-student_privacy",
    "utc": "2026-09-29T15:55:58.344030+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 123,
    "fixture_id": "cycle-06-doc-document_ingestion",
    "utc": "2026-09-29T15:56:00.238576+00:00",
    "status": 200,
    "latency_seconds": 0.8621233210142236,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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  {
    "condition": "A",
    "number": 127,
    "fixture_id": "cycle-07-query-q_tutoring_bus",
    "utc": "2026-09-29T15:56:01.832456+00:00",
    "status": 200,
    "latency_seconds": 0.11594053197768517,
    "dimensions": 1024,
    "norm": 1.0000000018880384,
    "runtime": "Ollama 0.18.0",
    "input": "On which tutoring day is a late bus available at Cedar School?",
    "input_sha256": "92f745eb769d73d71e9891b64d0272d9ea216cea882f0b3e96a051b9d5675623",
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    "fixture_id": "cycle-07-doc-fraction_number_line",
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    "fixture_id": "cycle-07-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "A",
    "number": 134,
    "fixture_id": "cycle-07-doc-reading_pilot",
    "utc": "2026-09-29T15:56:05.847052+00:00",
    "status": 200,
    "latency_seconds": 1.2898956770077348,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "number": 136,
    "fixture_id": "cycle-07-doc-teacher_feedback",
    "utc": "2026-09-29T15:56:07.801383+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 137,
    "fixture_id": "cycle-07-doc-library_access",
    "utc": "2026-09-29T15:56:08.792918+00:00",
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    "fixture_id": "cycle-07-doc-attendance_review",
    "utc": "2026-09-29T15:56:10.605906+00:00",
    "status": 200,
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    "utc": "2026-09-29T15:56:12.602901+00:00",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 142,
    "fixture_id": "cycle-07-doc-document_ingestion",
    "utc": "2026-09-29T15:56:13.493036+00:00",
    "status": 200,
    "latency_seconds": 0.8897538660094142,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
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      "total_tokens": 411
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  {
    "condition": "A",
    "number": 143,
    "fixture_id": "cycle-07-doc-service_health",
    "utc": "2026-09-29T15:56:14.526593+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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  {
    "condition": "A",
    "number": 153,
    "fixture_id": "cycle-08-doc-reading_pilot",
    "utc": "2026-09-29T15:56:18.885237+00:00",
    "status": 200,
    "latency_seconds": 1.1812833419826347,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
    "usage": {
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    "number": 155,
    "fixture_id": "cycle-08-doc-teacher_feedback",
    "utc": "2026-09-29T15:56:20.795544+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "condition": "A",
    "number": 156,
    "fixture_id": "cycle-08-doc-library_access",
    "utc": "2026-09-29T15:56:21.820789+00:00",
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    "input": "Section 1 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 2 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 3 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 4 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 5 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.",
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    "condition": "A",
    "number": 158,
    "fixture_id": "cycle-08-doc-attendance_review",
    "utc": "2026-09-29T15:56:23.641658+00:00",
    "status": 200,
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 161,
    "fixture_id": "cycle-08-doc-document_ingestion",
    "utc": "2026-09-29T15:56:26.500897+00:00",
    "status": 200,
    "latency_seconds": 0.8956150470185094,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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      "total_tokens": 411
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  {
    "condition": "A",
    "number": 162,
    "fixture_id": "cycle-08-doc-service_health",
    "utc": "2026-09-29T15:56:27.560796+00:00",
    "status": 200,
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    "fixture_id": "cycle-09-doc-fraction_number_line",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "A",
    "number": 172,
    "fixture_id": "cycle-09-doc-reading_pilot",
    "utc": "2026-09-29T15:56:31.981708+00:00",
    "status": 200,
    "latency_seconds": 1.2744819960207678,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "number": 174,
    "fixture_id": "cycle-09-doc-teacher_feedback",
    "utc": "2026-09-29T15:56:34.086992+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "number": 175,
    "fixture_id": "cycle-09-doc-library_access",
    "utc": "2026-09-29T15:56:35.093991+00:00",
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    "number": 177,
    "fixture_id": "cycle-09-doc-attendance_review",
    "utc": "2026-09-29T15:56:36.865524+00:00",
    "status": 200,
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    "fixture_id": "cycle-09-doc-math_exit_ticket",
    "utc": "2026-09-29T15:56:38.775933+00:00",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 180,
    "fixture_id": "cycle-09-doc-document_ingestion",
    "utc": "2026-09-29T15:56:39.719349+00:00",
    "status": 200,
    "latency_seconds": 0.9430144729849417,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
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  {
    "condition": "A",
    "number": 181,
    "fixture_id": "cycle-09-doc-service_health",
    "utc": "2026-09-29T15:56:40.695883+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "runtime": "Ollama 0.18.0",
    "input": "How should third graders represent one eighth and one sixth on a number line?",
    "input_sha256": "5e3b4146885b38a7e7d56551c23fce11141328e614648da64ac6b5074c939944",
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    "fixture_id": "cycle-10-doc-equivalent_fractions",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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  {
    "condition": "A",
    "number": 191,
    "fixture_id": "cycle-10-doc-reading_pilot",
    "utc": "2026-09-29T15:56:44.962847+00:00",
    "status": 200,
    "latency_seconds": 1.2299569649912883,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
    "usage": {
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    "number": 193,
    "fixture_id": "cycle-10-doc-teacher_feedback",
    "utc": "2026-09-29T15:56:47.663049+00:00",
    "status": 200,
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 2 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 3 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 4 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.\n\nSection 5 of Library access pilot. In a fictional school library survey, 68 of 100 responding students could visit after school on Tuesday while 22 could visit only at lunch. The librarian can supervise an extra Tuesday hour if another staff member covers lunch. No extra staffing allocation has been approved. The principal asks for a pilot that does not reduce lunchtime access. A recommendation must keep the staffing condition visible and avoid inventing a budget.",
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    "condition": "A",
    "number": 196,
    "fixture_id": "cycle-10-doc-attendance_review",
    "utc": "2026-09-29T15:56:50.652350+00:00",
    "status": 200,
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "A",
    "number": 199,
    "fixture_id": "cycle-10-doc-document_ingestion",
    "utc": "2026-09-29T15:56:54.049906+00:00",
    "status": 200,
    "latency_seconds": 0.9129301949869841,
    "dimensions": 1024,
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    "runtime": "Ollama 0.18.0",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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      "total_tokens": 411
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  {
    "condition": "A",
    "number": 200,
    "fixture_id": "cycle-10-doc-service_health",
    "utc": "2026-09-29T15:56:55.101726+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 20,
    "fixture_id": "cycle-01-doc-reading_pilot",
    "utc": "2026-09-29T16:05:45.240279+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "fixture_id": "cycle-01-doc-student_privacy",
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    "number": 28,
    "fixture_id": "cycle-01-doc-document_ingestion",
    "utc": "2026-09-29T16:05:50.575749+00:00",
    "status": 200,
    "latency_seconds": 0.6691461279988289,
    "dimensions": 1024,
    "norm": 0.9999994835302829,
    "runtime": "OVMS/OpenVINO 2026.5, NPU",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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      "total_tokens": 411
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  {
    "condition": "B",
    "number": 29,
    "fixture_id": "cycle-01-doc-service_health",
    "utc": "2026-09-29T16:05:51.256284+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 39,
    "fixture_id": "cycle-02-doc-reading_pilot",
    "utc": "2026-09-29T16:05:57.867849+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "fixture_id": "cycle-02-doc-teacher_feedback",
    "utc": "2026-09-29T16:05:59.183030+00:00",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "B",
    "number": 45,
    "fixture_id": "cycle-02-doc-student_privacy",
    "utc": "2026-09-29T16:06:01.881424+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "B",
    "number": 47,
    "fixture_id": "cycle-02-doc-document_ingestion",
    "utc": "2026-09-29T16:06:03.221408+00:00",
    "status": 200,
    "latency_seconds": 0.6711830369895324,
    "dimensions": 1024,
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    "runtime": "OVMS/OpenVINO 2026.5, NPU",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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  {
    "condition": "B",
    "number": 48,
    "fixture_id": "cycle-02-doc-service_health",
    "utc": "2026-09-29T16:06:03.910553+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 58,
    "fixture_id": "cycle-03-doc-reading_pilot",
    "utc": "2026-09-29T16:06:10.534707+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "fixture_id": "cycle-03-doc-attendance_review",
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
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  {
    "condition": "B",
    "number": 67,
    "fixture_id": "cycle-03-doc-service_health",
    "utc": "2026-09-29T16:06:16.473347+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 77,
    "fixture_id": "cycle-04-doc-reading_pilot",
    "utc": "2026-09-29T16:06:23.105307+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "B",
    "number": 83,
    "fixture_id": "cycle-04-doc-student_privacy",
    "utc": "2026-09-29T16:06:27.137422+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "B",
    "number": 85,
    "fixture_id": "cycle-04-doc-document_ingestion",
    "utc": "2026-09-29T16:06:28.469138+00:00",
    "status": 200,
    "latency_seconds": 0.6715527409978677,
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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  {
    "condition": "B",
    "number": 86,
    "fixture_id": "cycle-04-doc-service_health",
    "utc": "2026-09-29T16:06:29.154850+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 96,
    "fixture_id": "cycle-05-doc-reading_pilot",
    "utc": "2026-09-29T16:06:35.817398+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "fixture_id": "cycle-05-doc-attendance_review",
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
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  {
    "condition": "B",
    "number": 105,
    "fixture_id": "cycle-05-doc-service_health",
    "utc": "2026-09-29T16:06:41.823299+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 115,
    "fixture_id": "cycle-06-doc-reading_pilot",
    "utc": "2026-09-29T16:06:48.413816+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "fixture_id": "cycle-06-doc-teacher_feedback",
    "utc": "2026-09-29T16:06:49.703438+00:00",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "B",
    "number": 121,
    "fixture_id": "cycle-06-doc-student_privacy",
    "utc": "2026-09-29T16:06:52.371687+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "B",
    "number": 123,
    "fixture_id": "cycle-06-doc-document_ingestion",
    "utc": "2026-09-29T16:06:53.696696+00:00",
    "status": 200,
    "latency_seconds": 0.6657048339839093,
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    "runtime": "OVMS/OpenVINO 2026.5, NPU",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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  {
    "condition": "B",
    "number": 124,
    "fixture_id": "cycle-06-doc-service_health",
    "utc": "2026-09-29T16:06:54.379640+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 134,
    "fixture_id": "cycle-07-doc-reading_pilot",
    "utc": "2026-09-29T16:07:00.923864+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
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  {
    "condition": "B",
    "number": 143,
    "fixture_id": "cycle-07-doc-service_health",
    "utc": "2026-09-29T16:07:06.950173+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 153,
    "fixture_id": "cycle-08-doc-reading_pilot",
    "utc": "2026-09-29T16:07:13.569064+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "fixture_id": "cycle-08-doc-teacher_feedback",
    "utc": "2026-09-29T16:07:14.842121+00:00",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "B",
    "number": 159,
    "fixture_id": "cycle-08-doc-student_privacy",
    "utc": "2026-09-29T16:07:17.439613+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "B",
    "number": 161,
    "fixture_id": "cycle-08-doc-document_ingestion",
    "utc": "2026-09-29T16:07:18.705803+00:00",
    "status": 200,
    "latency_seconds": 0.629885069996817,
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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  {
    "condition": "B",
    "number": 162,
    "fixture_id": "cycle-08-doc-service_health",
    "utc": "2026-09-29T16:07:19.389881+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "number": 172,
    "fixture_id": "cycle-09-doc-reading_pilot",
    "utc": "2026-09-29T16:07:25.991028+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "fixture_id": "cycle-09-doc-attendance_review",
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    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
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  {
    "condition": "B",
    "number": 181,
    "fixture_id": "cycle-09-doc-service_health",
    "utc": "2026-09-29T16:07:31.914129+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
    "input_sha256": "61283fbf42e7d59837173b5e52a0bf69ba0d633578068ecbea1fa02f4f82064e",
    "usage": {
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    "input": "Section 1 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 2 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 3 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 4 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.\n\nSection 5 of Equivalent fractions with area models. A grade 6 teacher prepares a short lesson on equivalent fractions. Students shade one half of a rectangle and then partition each half into two equal pieces to see two fourths. The activity compares area models and number lines, and asks students to justify equality without relying on a memorized multiply-the-top-and-bottom rule. The teacher checks whether learners preserve the same whole when they compare diagrams.",
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    "condition": "B",
    "number": 191,
    "fixture_id": "cycle-10-doc-reading_pilot",
    "utc": "2026-09-29T16:07:38.481502+00:00",
    "status": 200,
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    "input": "Section 1 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 2 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 3 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 4 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.\n\nSection 5 of Reading intervention pilot. A fictional district ran an eight-week reading support pilot in four grade 7 classrooms with 120 students. The reading benchmark share rose from 42 percent to 58 percent; the math benchmark changed from 51 percent to 54 percent. Attendance fell from 91 percent to 89 percent. There was no comparison group and no random assignment, so the observed improvement does not establish that the pilot caused the increase. Staff plan to examine attendance and student work before expanding.",
    "input_sha256": "fbf56bd410a2561cac9923d5354a0b780aa5068380a9a894dbbaf14fccfb5220",
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    "fixture_id": "cycle-10-doc-teacher_feedback",
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    "input": "Section 1 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 2 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 3 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 4 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.\n\nSection 5 of Feedback on distribution errors. A student solves three times the quantity x plus two equals eighteen by writing three x plus two equals eighteen. The teacher identifies the missing distribution to the constant: three times x plus three times two is three x plus six. Subtract six to obtain three x equals twelve and divide by three to obtain x equals four. Supportive feedback names the exact step and asks the student to check by substitution.",
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    "input": "Section 1 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 2 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 3 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 4 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.\n\nSection 5 of Attendance review protocol. A school support team reviews weekly attendance patterns before assigning interventions. The team compares absences across weeks, asks whether transportation or illness explains missed days, and contacts families through the approved school channel. A single missed day is not enough to infer a persistent barrier. Staff record the source and date of each observation and avoid adding unsupported claims to a student summary.",
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    "condition": "B",
    "number": 197,
    "fixture_id": "cycle-10-doc-student_privacy",
    "utc": "2026-09-29T16:07:42.466733+00:00",
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    "input": "Section 1 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 2 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 3 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 4 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.\n\nSection 5 of Synthetic benchmark data handling. A controlled benchmark uses fictional school examples and no live student records. Prompts, expected answers, document chunks, and model outputs are stored under a local run identifier. Reviewers keep model identity hidden while scoring answer quality. The retrieval index is built in the Windows controller and never written to a tenant database. This protocol makes comparison repeatable without exposing production student information.",
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    "input": "Section 1 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 2 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 3 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 4 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.\n\nSection 5 of Math exit ticket design. A teacher uses a three-question exit ticket after a lesson on fractions. One item asks students to place one sixth on a number line, another asks them to compare one sixth with one eighth, and the final item asks for a short explanation using equal partitions. The teacher sorts responses by misconception, correct reasoning, and unclear work. The next lesson opens with a visual model for students who confused larger denominators with larger unit fractions.",
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  {
    "condition": "B",
    "number": 199,
    "fixture_id": "cycle-10-doc-document_ingestion",
    "utc": "2026-09-29T16:07:43.764173+00:00",
    "status": 200,
    "latency_seconds": 0.6630473090044688,
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    "runtime": "OVMS/OpenVINO 2026.5, NPU",
    "input": "Section 1 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 2 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 3 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 4 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.\n\nSection 5 of Document ingestion workflow. A document ingestion workflow splits a synthetic school handbook into stable chunks with document identifiers and section labels. It embeds each chunk, stores vectors in a local temporary index, and checks that every vector has the expected dimension. The workflow measures chunk throughput and failed inputs separately from interactive question latency. It never modifies production Qdrant or PostgreSQL collections on the live Partner Demo Server.",
    "input_sha256": "3899a022b2a8ad9f09961b5c2421c0c03af610ffaf84fe44a97d0a9b53d1503b",
    "usage": {
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  {
    "condition": "B",
    "number": 200,
    "fixture_id": "cycle-10-doc-service_health",
    "utc": "2026-09-29T16:07:44.440489+00:00",
    "status": 200,
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    "input": "Section 1 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 2 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 3 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 4 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.\n\nSection 5 of Appliance service health checks. A safe appliance benchmark observes free memory, swap-out activity, model-service responses, and the status of existing tenant containers. The watcher stops only benchmark-generated requests when pressure crosses a threshold. It does not restart OpenWebUI, Docker, Colima, Qdrant, PostgreSQL, or any tenant service. The controller preserves completed responses and a safety-event log so an operator can review the cause before deciding on another stage.",
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