docs: record live model review proof
GeoIntel CI / docs-smoke (push) Canceled after 0s
GeoIntel CI / contract-smoke (push) Canceled after 0s

This commit is contained in:
Codex
2026-07-15 03:40:59 +02:00
parent 875872131c
commit c2d7dd48a8
2 changed files with 19 additions and 0 deletions
+3
View File
@@ -24,6 +24,9 @@
trained, downloaded, activated or reconfigured.
- Clarified map quality output with strict match counts and the existing
diagnostic reference-envelope match, without changing canonical QA metrics.
- Live Mol proof measured 282 candidates, 209 strict matches, precision 74.1%,
recall 68.5% and F1 71.2%; 212 diagnostic envelope matches made the remaining
three possible box/footprint differences explicit.
## Sprint 197 Measured detection accuracy and durable review (2026-07-15)
+16
View File
@@ -8227,6 +8227,22 @@ Known limitation:
independently collected training-only AOIs and complete tile labels; holdout
review evidence must not be recycled into training.
Live operational proof:
- Deployed commits `919e102` and packaging fix `8758721` to the Tower
all-in-one runtime. The container became healthy, PostGIS reported 3.6,
Alembic remained at `202607150001`, and the frontend/API/icon proxy smoke
passed.
- Both review validators ran inside the rebuilt image against persistent
operator data with `status=complete`: 5 confirmed model false positives and
10 confirmed model false negatives, with zero unreviewed cards.
- A new 15.31 ha browser-drawn Mol selection persisted 282 candidates and 209
strict GRB matches. It measured precision `0.741`, recall `0.685`, F1 `0.712`,
73 false positives and 96 false negatives. The diagnostic reference-envelope
pass found 212 matches and only 3 possible box/footprint differences.
- Browser checks at 1280x720 and 3440x1440 showed the strict and diagnostic
values without horizontal overflow. The ultrawide map used about 2,408 px;
the browser console contained no warnings or errors.
Next:
- Collect a new training-only small-building/background evidence pack outside
all operational holdouts, then train an inactive candidate only if the pack