- Mark duct closest to endpoint elevations as likely signal path
- Likely duct drawn with solid borders, bold label, stronger fill
- Other ducts shown dimmed with dashed borders
- Fix path info layout: table columns prevent El wrapping
- Surface observations as compact table, limited to 5 nearest stations
- Use all HRRR profiles along path (start, mid, end) for duct detection
- Fall back to sounding ducts when HRRR too coarse to detect layers
- Merge overlapping ducts, show top 3 strongest with source labels
- Limit surface observations to 5 closest stations along path
- Compact solar conditions as inline row
- Compact band/mode/distance info line
- Fix button text to "Flag as Invalid"
- Overlay HRRR duct layers on elevation profile chart (green bands)
- Add flagged_invalid field to contacts with toggle button on detail page
- Fix prune_old_grid_profiles to only delete 0.125° grid profiles,
preserving QSO-linked HRRR data at arbitrary positions
- Auto-enqueue HRRR fetch when viewing contact without HRRR data (<48h)
- Compact solar conditions and contact details layout
- Add band/mode/distance info line to elevation profile section
- Leaflet map on contact detail pages showing both station locations
- SRTM elevation profile with Chart.js: terrain, LOS, Fresnel zone
- Path info: distance, azimuth, elevation angle in both directions
- Compact 4-column grid layout for contact details
- Contact map page with all contacts plotted
- Rename QSOs to Contacts in page header
- Wrap algo page in standard layout
- All menus and UI text now say Contacts instead of QSOs
- Add 68, 122, 134, 241 GHz bands to submit form and validation
- Add info box on submit page explaining why contacts matter
- Larger submit button with icon
- Make HRRR partition migration idempotent for partial re-runs
- 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
Rename all modules, functions, variables, routes, and UI text from
qso/qsos to contact/contacts. Database table stays as "qsos" to avoid
migration. Add /qsos -> /contacts redirects for old URLs.
SSB is not possible on rainscatter - the original analysis was wrong.
The 24 GHz PH advantage is entirely a contest strategy artifact: Great
Lakes operators line up on opposite shores for rapid-fire SSB contacts.
With EN/CM/DM cluster activity removed, CW leads by 16% at 24 GHz.
CW advantage is monotonically increasing with frequency as physics
predicts. No 24 GHz anomaly exists.
Move COPY priv after mix compile so ML model retraining doesn't bust
the compile cache. Create empty priv dirs before compile for
:code.priv_dir. Move algo.md after compile. Add docs/plans and
analysis outputs to .dockerignore.
CW advantage scales dramatically with frequency: +29% at 10G, +48% at
47G, +221% at 75G. At 24 GHz the pattern reverses (PH wins by 8%)
due to rainscatter favoring wider-bandwidth modes. Above 122 GHz,
100% of contacts are CW — SSB can't close the path.
The ML model undervalues conditions outside Aug/Sep training data
(e.g. April with excellent factors scored 37/100). Algorithm's
physics-based factors handle unseen seasons correctly.
- Algorithm is primary scorer, ML infrastructure kept for iteration
- Remove unused ML grid worker code path
- Add client-side propagation reach: BFS flood-fill from clicked point
through contiguous cells with score >= 50, drawn as convex hull polygon
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
Add SFI, Kp max (solar), K-index, lifted index (sounding stability),
and ducting_detected (HRRR) as model features. Training now joins to
solar_indices and nearest sounding (within 6 hours) for both phases.
Model can learn solar/geomagnetic effects if they exist in the data.
- 15 features: add surface_refractivity and latitude
- Bigger network: 128→64→32 (3 hidden layers)
- Phase 1: pretrain on 500K stratified algorithm scores (all seasons/locations)
- Phase 2: fine-tune on 57K real QSO-HRRR matched data (percentile target)
- Lower LR (0.0003) for fine-tuning to preserve pretrained knowledge
- Model.train accepts :initial_state option for transfer learning
- 3 hidden layers instead of 2 for better feature interaction learning
- Target is within-band distance percentile (0-1) instead of raw
normalized distance — reduces noise from operator/equipment variation
Features: HRRR conditions averaged at both QSO endpoints + solar time
Target: distance_km normalized per-band (distance / p99_range, capped at 1.0)
This trains on actual propagation outcomes from 57K+ QSOs, not the
hand-tuned algorithm output.
Raw features had vastly different scales (pressure ~1013, sin/cos ~[-1,1])
causing gradient explosion. Normalize all atmospheric features to ~[0,1]
using known physical bounds. Add Polaris dep for optimizer.
- Add polaris dep for Adam optimizer (Axon.Optimizers deprecated)
- Fix Model.train/3 to use Polaris.Optimizers.adam
- Training task: mix propagation_train with stratified band sampling
- Model encodes solar time (longitude/15) not fixed timezone
Solar time (longitude/15) replaces fixed CDT/CST offset for time-of-day
scoring. Correlation analysis shows dramatic improvement at higher
frequencies: 24 GHz rho jumps from 0.056 (UTC) to 0.188 (solar), and
75 GHz corrects from spurious -0.39 to physically correct +0.24.
Score time-of-day per grid point using longitude/15 solar offset instead of
hardcoded CST/CDT. Add PWAT as 10th scoring factor. Refine pressure thresholds.
Update ML model and training pipeline to use local solar time.
Comprehensive description of atmospheric science behind the predictions
for meteorologist review: refractivity-propagation connection, four
ducting mechanisms, HRRR model usage, frequency-dependent effects,
diurnal/seasonal cycles, and known limitations.
- point_detail response includes forecast array (score per valid_time)
- SVG sparkline shows score trend across all forecast hours
- Trend indicator: Improving/Declining/Steady based on first vs last score
- Add covering index (band_mhz, lat, lon, valid_time) INCLUDE (score) for
point_forecast queries
