COVEN: Providing a Variety of Threshold-Based Forecasts for the Outer Radiation Belt
Bibliographic record
Abstract
Abstract We present a suite of VAMPIRE (Van Allen belt Multi‐day Predictions by Implementing a Random Forest for Electrons) models capable of predicting if the outer radiation belt crosses set percentile thresholds. We use Random Forest classification models to predict if the daily ∼2 MeV electron flux level across the outer radiation belt exceeds thresholds from the 60th to the 95th percentiles. Most models show a balanced accuracy of >0.7 (>70%) at nowcasting and ∼0.6 (60%) at forecasting up to 6 days in advance, a longer forecast than current operational models. Using feature importance (mean decrease in impurity), we determine the key inputs that are important in driving increasing flux levels and over what timescales they have an impact. Crucially, we find that only the average AL index from various days beforehand is required to be able to forecast radiation belt fluxes with good skill, meaning that models such as these could be operationally viable for space weather stakeholders. We call this suite a Collection Of VAMPIRE models for Enhanced Notification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".