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Loop 77: Phase F F7 — calibration link from negative-result entries to intake reports - #763
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…ntake reports Closes the learning loop by tracing each negative-result entry back to its prediction-vs-actual data in the calibration intake report. - schema: add intake_report_id to negative_result_entry.schema.json - script: link_negative_result_to_intake.py — CLI that reads archive + intake report, links by candidate_id, reports matched/unmatched entries, orphan detection, lab summary, intake_report_id validation - tests: 25 tests covering linkage, validation, CLI, error paths - docs: METRICS_CURRENT.md, ROADMAP.md updated - baseline: 2822 tests
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Change
Phase F F7 implementation: calibration link from negative-result entries to intake reports.
What
intake_report_idoptional field tonegative_result_entry.schema.jsonscripts/link_negative_result_to_intake.py— reads a negative-result archive JSON + calibration intake report JSON, links entries by candidate_id, and produces a structured link report (matched/unmatched counts, linkage rate, orphan detection, per-candidate predictions + lab summary, intake_report_id validation, cohort metrics)tests/evidence/test_link_negative_result_to_intake.pyWhy
Closes the learning loop (F7). Negative-result entries can now be traced back to their calibration intake data, making it possible to see what predictions and lab results produced each failure.
Evidence
make agent-checkpassesSafety impact
None. This is an informational linking tool. It does not change scoring, selection, or release workflows.
Proof level
N/A — infrastructure/CI improvement.
Limitations
Human review needed
No