Extremely rapid radiation belt electron losses across the magnetopause
Bibliographic record
Abstract
Introduction The Earth’s radiation belt environment is highly dynamic, with different processes acting on various particle populations over a range of timescales. Constraining the timescales over which these processes act is essential to both our physical understanding of the radiation belt environment and our ability to predict and mitigate space weather effects. Methods In this study, we leverage the GPS constellation to evaluate a radiation belt dropout that occurred on 14 May 2019, combining observations from 18 GPS satellites during this event to evaluate the dominant loss mechanism of the dropout and to constrain the timescale of this loss. This dropout affected the entire relativistic electron population, abruptly depleting the 4 MeV population by an order of magnitude following a strong magnetopause compression. Results We identify magnetopause shadowing as the dominant loss mechanism during this dropout through analysis of the electron flux data and the temporal evolution of the electron phase space density with respect to the last closed drift shell. The K = 0.14 R E G 1 / 2 , μ = 3433 M e V / G electron population was eliminated within 30 min at 4.8 ≤ L * < 4.9 and eliminated in 126 min at 4.5 ≤ L * < 4.6 . Discussion Dropout events are typically understood to occur on timescales of several hours to a day and sub-hour dropouts have previously only been reported by a handful of studies, so this is an exceptionally rapid elimination of the relativistic population. Our results therefore reinforce that radiation belt dropouts can occur on sub-hour timescales and highlight the value of the GPS constellation (which now contains 25 satellites distributed across magnetic local time) on studying these rapid, large-scale dynamics in the Earth’s radiation belts.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".