57,186 prod contacts stored pos1/pos2 with 'lng'; 1,133 used 'lon'.
Every Elixir caller carried a `pos["lon"] || pos["lng"]` fallback
— which just caused a SQL widget to silently miscount 98% of contacts
(count_narr_done used `pos1->>'lon'` directly, no fallback, so every
lng-keyed row returned NULL and failed the coverage check).
- Migration rewrites every pos1/pos2 JSONB in place, renaming 'lng' to
'lon' and dropping 'lng'.
- Removes all 20+ `|| pos["lng"]` fallbacks across lib/, workers,
scorer, weather, radio.ex, contact show view, and recalibrator.
- lib_ml/propagation_analyze.ex SQL now reads pos1->>'lon' directly
(was reading 'lng' only, which would have broken after migration).
- priv/repo/import_contacts.exs one-time seed script now emits 'lon'
with string keys, matching production shape.
- Test fixtures in 4 test files normalized to 'lon'.
- Two lng-characterization tests deleted — nonsensical post-normalize.
- Updated notebook + old import_weather script to match.
- JS hook contact_map_hook.ts TypeScript type narrowed to 'lon'.
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.
- Nx, Axon, EXLA, Polaris deps restricted to only: [:dev, :test]
- model.ex and training mix tasks moved to lib_ml/ (compiled via
elixirc_paths in dev/test only)
- load_ml_model uses Code.ensure_loaded? + apply/3 to avoid
compile-time references to ML modules in production
- Verified: MIX_ENV=prod compiles clean with no ML warnings
