Replaces the two-phase (algorithm-scores pretrain + QSO-distance
finetune) pipeline with a single unbiased phase that samples
hrrr_profiles stratified uniformly by calendar month and trains on the
physical algorithm score from Scorer.composite_score/2.
Previous Phase 2 target was 'within-band distance percentile' computed
from the contacts table. Because non-contest months have almost no
QSOs, the model learned 'these conditions → QSO happened → good' and
implicitly 'no QSO → bad', so it systematically under-predicted
propagation during quiet months (Feb, Mar, Nov). The new pipeline
decouples the target from QSO activity entirely.
Stratification uses ROW_NUMBER() OVER (PARTITION BY month ORDER BY
random()) so every month contributes the same number of profile rows
regardless of contest-driven density. Each profile is exploded into
one training row per band.
Trained model: val RMSE=1.66 pts, R²=0.9744 on 840k rows (10k
profiles/month × 7 bands, 50 epochs). Monthly sanity check at fixed
weather conditions (10 GHz, 15°C/10°C) now shows Jan=Feb=Mar=68 — the
model no longer penalizes quiet months.
ML Integration:
- Load trained model at app startup, cache compiled predict fn in persistent_term
- Grid worker uses batched ML prediction (10K chunks) when model loaded,
falls back to algorithm scorer when not
- ML score replaces composite, algorithm factor scores preserved for detail view
- Fix process explosion: single EXLA call per chunk instead of per-grid-point
QSO Features:
- Callsign search (ILIKE on station1/station2) with trigram indexes
- Reciprocal QSO grouping (same pair, same band, same hour)
- Wider layout (max-w-7xl) for data table pages
- QSO Training Data link on map page
Infrastructure:
- Re-enable hourly propagation grid worker in dev
- Track ML model weights in git for Docker builds
- Add btree indexes on qsos (timestamp, band, distance_km)
- Remove nav icons from layout header
