Predicting Substorm Onsets From Auroral Dynamics Using Machine Learning
Bibliographic record
Abstract
Vi forsøker å predikere substorm onsets ved bruk av serier av bilder av nordlys, og anvender enkelte maskinlæringsteknikker som trekksekstraksjon av ferdigtrente konvolusjons-nevrale nettverk (CNN), hovedkomponentanalyse (PCA) og k-means som en ikke-veiledet læringsmetode. Analysen ble gjennomført på data fra et all-sky kamera i Fort Simpson i Canada, i perioden 1. oktober 2012 til 28. februar 2013. Våre resultater indikerer at man kan predikere substorm onsets til en viss grad, hvor en av våre beste modeller fikk en balansert nøyaktighet på over 62%, med en sann positive rate på ∼68% og en falsk positive-rate på ∼43%. Vi filtrerer derimot ikke ut bilder som inneholder en overskyet himmel eller månen, og våre resultater indikerer at disse faktorene spiller en stor rolle når det kommer til å forstyrre våre modellers evne til predikere substorm onsets
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".