Empirical reconstruction of pre-1995 extreme storms using ML-derived solar wind inputs
Bibliographic record
Abstract
The geomagnetic field, electric currents, and storm-time plasma pressure are reconstructed for extreme storms before 1995: the July 1982 superstorm and the March 1989 Hydro-Québec grid collapse event. The reconstructions are based on an improved magnetic field data mining method that fills gaps in the solar wind data using a recently published machine learning algorithm. The data mining reconstructions are rescaled using statistics of the nearest neighbor bins to eliminate the bias toward weaker storms. A concurrent reconstruction method provides the combined description of storms and substorms: storm and substorm features are first reconstructed independently for the inner and tail magnetosphere, respectively, and then the data fitting is reiterated using synthetic data generated using the first round of reconstructions. The data fitting procedure is further tuned to better resolve the location of the field-aligned currents. Testing the updated methods for the November 2003 and 1982 superstorms significantly improves the validation results for in-situ observations. The effect of rescaling doubles the peak ring current density (from 81 to 168 nA/m 2 for the November 2003 storm) while the tuned fitting procedure shifts the Region-2 field-aligned currents equatorward to magnetic latitudes as low as 50°. Rescaling also intensifies the equatorial currents such that X-line arcs and even an X-loop are formed within geosynchronous orbit, where reconnection may approach a relativistic regime. Such a change in the field topology limits the peak plasma pressure obtained from the quasi-static force balance equation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".