let an operator's adjudication reach the score

The review vocabulary already separates a model error from a reference gap,
because the product's position is that official footprints are not
automatically perfect ground truth. Those verdicts were only counted. An
operator who inspected forty false positives and established that twelve are
buildings the reference simply lacks still saw a precision counting all forty
against the model — a number they had personally disproved, on the panel where
they disproved it.

Applying the verdicts gives an adjudicated score reported next to the raw one,
so nothing is quietly improved. Not being able to judge is not evidence in the
model's favour, so uncertain and obscured verdicts keep counting, as does a
decision from a later release that this runtime does not recognise.

Because part of the evidence is usually still unreviewed, the honest form is an
interval rather than a single corrected number: pessimistic assumes every
unreviewed finding is a model error, optimistic assumes none is, and the
headline equals the pessimistic reading so a partly reviewed check never
presents as a settled one.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

backend/app/schemas/detection_review.py

@@ -53,6 +53,10 @@ class DetectionReviewSummary(BaseModel):
     false_positive_total: int
     false_negative_total: int
     decision_counts: dict[str, int]
+    # The score with the operator's verdicts applied, next to the raw one. A
+    # finding adjudicated as a reference gap is not the model's error, and an
+    # interval covers what the unreviewed remainder could still turn out to be.
+    reviewed_metrics: dict | None = None
 
 
 class DetectionReviewList(BaseModel):