A scheme for forecasting severe space weather
Bibliographic record
Abstract
Abstract A scheme is suggested and tested for forecasting severe space weather (SvSW) using solar wind velocity (V) and the north‐south component (Bz) of the interplanetary magnetic field (IMF) measured using the ACE (Advanced Composition Explorer) satellite from 1998 to 2016. SvSW has caused all known electric power outages and telegraph system failures. Earlier SvSW events such as the Carrington event of 1859, Quebec event of 1989 and an event in 1958 are included with information from the literature. Dst storms are used as references to identify 89 major space weather events (DstMin ≤ −100 nT) in 1998–2016. The coincidence of high coronal mass ejection (CME) front (or CME shock) velocity ΔV (sudden increase in V over the background by over 275 km/s) and sufficiently large Bz southward at the time of the ΔV increase is associated with SvSW; and their product (ΔV × Bz) is found to exhibit a large negative spike at the speed increase. Such a product (ΔV × Bz) exceeding a threshold seems suitable for forecasting SvSW. However, the coincidence of high V (not containing ΔV) and large Bz southward does not correspond to SvSW, indicating the importance of the impulsive action of large Bz southward and high ΔV coming through when they coincide. The need for the coincidence is verified using the CRCM (Comprehensive Ring Current Model), which produces extreme Dst storms (<DstMP> < −250 nT) characterizing SvSW when there is coincidence.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".