Promote expanded building detector profile

docs/TODO.md

@@ -121,7 +121,7 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Export and audit AOI1024 clean-label variants; select `yolo-building-aoi1024-visible050-minpx8` as the first audit-passing 512px training candidate.
 - [x] Train and gate `geointel-building-yolov8s-aoi1024clean512e50-pt` through seven positive AOIs and nine hard-negative/background AOIs.
 - [x] Train and gate background-aware `geointel-building-yolov8s-aoi1024bg512r3e50-pt`; it is the strongest positive-AOI candidate so far but remains inactive because full background-candidate false-positive pressure still blocks default promotion.
-- [x] Add explicit operator detection profiles for local model assets: balanced review around threshold `0.15` remains candidate-only, while conservative high-precision review around threshold `0.35` is marked promoted after the pure-empty split-background gate passed.
+- [x] Add explicit operator detection profiles for local model assets and promote the expanded-AOI balanced review profile at threshold `0.15` after positive and pure-empty split-background gates passed.
 - [x] Add pure-empty versus sparse-building contextual background corpus classification to operator manifests, hard-negative matrix filters and YOLO tile provenance.
 - [x] Add a split background-corpus matrix runner and report builder that runs pure-empty and sparse-context matrices separately.
 - [x] Teach the model promotion report to consume split background summaries so only `pure_empty_negative` blocks default promotion and `sparse_building_context` stays review-only.
@@ -135,8 +135,9 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Regenerate the AOI1024 cleanpx YOLO dataset with low-variance negative filtering and rerun visual contact-sheet QA before training.
 - [x] Train one inactive candidate from the filtered AOI1024 cleanpx YOLO dataset and gate it through the positive-AOI plus split-background promotion workflow; reject it because mean positive F1 remains below gate.
 - [x] Add deterministic dataset/base/trained-model SHA256 provenance to future operator training summaries.
-- [ ] Review per-AOI false-negative evidence for the weakest AOIs and improve positive sample/label geometry coverage before another training candidate.
-- [ ] Apply promoted V1 default building detector only after explicit operator review of the emitted `.env` updates, followed by rebuild/restart and browser/runtime smoke.
+- [x] Review per-AOI false-negative evidence, expand positive sample/label coverage and verify the resulting candidate improves false-negative rate in every validated AOI.
+- [ ] Complete rebuild/restart and browser/runtime smoke for the guarded promoted V1 building detector activation.
+- [ ] Expand focused small-building training evidence only after reviewing persistent false negatives from the promoted model; do not start another blind training run.
 
 ## Sprint 8 status