Dipolarization-induced energetic electron precipitation during continuously active periods and isolated substorms
Bibliographic record
Abstract
Recently we proposed a spatio-temporal model of auroral absorption for isolated substorms based on linear prediction filter technique, which describes the precipitation effects as a sum of properly weighted and time-delayed contributions of short dipolarizations/injections inferred from magnetic MPB index. Here we apply a similar approach to a more general and practically important type of continuous activity including substorms clusters, which is accompanied by intense energetic electron precipitation lasting for many hours and may affect ozone concentration and climate. Unexpectedly, in spite of very different geophysical background, the derived precipitation response to unit-scale injection appeared almost identical to that obtained for isolated substorms. Significant part of absorption variance during active non-storm periods turns out to be the result of superposition of previous injections with short memory less than 4 h. Our results indicate that, while the injection efficiency is roughly the same, large difference in precipitated fluxes and ionospheric response between two different types of activity is mostly provided by a more frequent appearance and increased intensities of dipolarizations/injections during active periods. In both event types dipolarizations are the decisive factor which determines energetic electron precipitation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".