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+# Calibration Review Policy
+
+OpenAMP Foundry should treat score calibration as a maintained claim, not a one-time benchmark artifact.
+
+A score can be useful today and misleading later if the dataset, baseline, reference set, or selection objective changes. This policy defines when calibration claims should be reviewed again.
+
+This is an evidence-governance document only. It does not add biological protocols, candidate-generation instructions, or optimization objectives.
+
+## What calibration means here
+
+Calibration means that a score, rank, threshold, or confidence label has an interpretable relationship to the decision it supports.
+
+Examples:
+
+- a threshold separates stronger candidates from weak controls in a declared benchmark slice;
+- a confidence label maps to a reproducible evidence level;
+- a score weight improves a shortlist decision against a cheap baseline;
+- a model output remains useful after novelty, feasibility, and safety filters.
+
+Calibration is not proof of biological activity.
+
+## Review triggers
+
+Recheck calibration when any of these changes:
+
+- dataset version;
+- benchmark split or benchmark slice;
+- reference set;
+- scoring weights;
+- novelty rules;
+- feasibility or safety filters;
+- external predictor version;
+- candidate-selection objective;
+- evidence certificate schema;
+- known failure slice;
+- accepted baseline.
+
+## Minimum calibration review note
+
+```markdown
+## Calibration review
+
+**Component:** Score, threshold, model, ranker, or confidence label.
+
+**Decision supported:** What decision depends on this calibration?
+
+**Last calibrated on:** Dataset, benchmark slice, and date or commit.
+
+**Trigger:** What changed?
+
+**Expected stability:** Why should the old calibration still hold, or why not?
+
+**Check performed:** Report, command, review method, or reason not run.
+
+**Result:** stable | narrowed | exploratory | blocked | follow-up required.
+
+**Decision impact:** What should change in ranking, threshold, confidence, or review status?
+```
+
+## Reviewer guidance
+
+Reviewers should ask for calibration review when a PR changes a component that downstream selection relies on.
+
+A PR may still merge without full recalibration if the component is clearly marked exploratory or if the change cannot affect decision weight.
+
+## Agent guidance
+
+Agents should not reuse old thresholds after changing the input distribution.
+
+Before promoting a score or threshold, an agent should identify the last calibration artifact and say whether the current change invalidates it.
+
+## Anti-patterns
+
+Avoid:
+
+- reusing a threshold after changing the dataset;
+- treating rank improvement as calibration;
+- hiding calibration limits in prose;
+- updating a scorer without updating the confidence boundary;
+- using a model output as if it were a calibrated probability when it is only a relative score.
+
+## Acceptance test
+
+A calibration review is adequate when a skeptical reviewer can answer:
+
+1. What decision depends on this score or threshold?
+2. What changed since the last calibration?
+3. Why should confidence stay the same or change?
+4. What status should the component carry now?
+
+If those answers are missing, the component should not become load-bearing.