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Use when running ELFQuake prospective real-data refresh scripts, including relabeling matured VLF rows and rebuilding real model input artifacts.
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Based on SOC occupation classification
| name | elfquake-data-refresh |
| description | Use when running ELFQuake prospective real-data refresh scripts, including relabeling matured VLF rows and rebuilding real model input artifacts. |
Run from the repository root. Keep data Italy-scoped, preserve raw records, normalize timestamps to UTC, and do not claim prediction value from refreshed data alone.
./scripts/refresh-prospective-labels.sh
./scripts/prepare-real-model-inputs.sh
rg -n "status|labeled_row_count|positive_count|negative_count" data/derived/models/*real_vlf_aligned_windows.readiness.json data/derived/multimodal/*labeled.readiness.json
Only run real training when readiness shows both positive and negative examples. If class variation is absent, report the blocker and keep collecting data.
Use these only when relevant:
./scripts/materialize-real-vlf-sequence.sh
./scripts/train-real-tabular-model.sh
After meaningful count or status changes, update docs/report.md and docs/next-actions.md concisely.
Use when running or regenerating ELFQuake sandpile simulations, synthetic seismic events, piezo/VLF-like signals, maps, videos, or simulation-derived model artifacts.
Use when running one-off ELFQuake source fetch, capture, normalization, or feature-build CLI commands for INGV, VLF, astronomy, geomagnetic, or space-weather data.
Use when training, comparing, diagnosing, or selecting ELFQuake synthetic PyTorch models, including multimodal sequence models and missing-modality ablation checks.