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feat: Phase F F7 negative result calibration link -- NCL- schema closes NRR-to-CAL learning loop, coverage consistency enforced, 63 tests - #902
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…es NRR-to-CAL learning loop, coverage consistency enforced, 63 tests (#901)
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Implements F7 from NEXT_100_PR_MAP: calibration link from negative-result entries to intake reports.
What
negative_result_calibration_link.py: NCL- prefixed dataclass linking NRR- records to CAL- intakeNegativeResultCalibrationLink: 10 fields (link_id, nrr_ids, intake_id, linked_at, link_type, link_rationale, batch_coverage_fraction, all_nrrs_linked, link_status, notes)VALID_LINK_TYPES: 5 values (batch_failure_feedback, single_candidate_rejection, systematic_failure_pattern, assay_protocol_failure, data_quality_rejection)VALID_LINK_STATUSES: 5 values (pending, submitted, accepted, rejected, superseded)validate_negative_result_calibration_link(): 14 rules including coverage consistency (all_nrrs_linked=True ↔ coverage=1.0)format_negative_result_calibration_link(): human-readable audit trailWhy
Negative results are only useful if they reach the calibration pipeline. Without an explicit link record, failures are silently discarded. The NCL- schema makes the NRR→CAL feedback loop an auditable artifact — you can tell from the record whether a batch of rejections was submitted for calibration intake.
Disconfirming pass