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
Replaces the one-shot startup Task with a GenServer that checks every
5 minutes whether propagation scores are older than 2 hours. If stale,
enqueues a PropagationGridWorker job (with dedup check to avoid double
queuing). Disabled in test env to avoid SQL Sandbox conflicts.
On app start, check if latest scores are older than 2 hours. If so,
immediately enqueue a PropagationGridWorker job. Covers app restarts,
deploys, and missed cron ticks.
Oban cron worker runs every 4 hours, finds QSOs without weather data,
discovers nearby ASOS/sounding stations, and enqueues WeatherFetchWorker
jobs. Migrations run automatically on app start in production.
