Rapid Outer Radiation Belt Flux Dropouts and Fast Acceleration during the March 2015 and 2013 Storms: Role of ULF Wave Transport from a Dynamic Outer Boundary
Bibliographic record
Abstract
We present simulations of the outer radiation belt electron flux during the March 2015 and March 2013 storms using a radial diffusion model. Despite differences in Dst intensity between the two storms the response of the ultra-relativistic electrons in the outer radiation belt was remarkably similar, both showing a sudden drop in the electron flux followed by a rapid enhancement in the outer belt flux to levels over an order of magnitude higher than those observed during the pre-storm interval. Simulations of the ultra-relativistic electron flux during the March 2015 storm show that outward radial diffusion can explain the flux dropout down to L*=4. However, in order to reproduce the observed flux dropout at L*<4 requires the addition of a loss process characterised by an electron lifetime of around one hour operating below L*~3.5 during the flux dropout interval. Nonetheless, during the pre-storm and recovery phase of both storms the radial diffusion simulation reproduces the observed flux dynamics. For the March 2013 storm the flux dropout across all L-shells is reproduced by outward radial diffusion activity alone. However, during the flux enhancement interval at relativistic energies there is evidence of a growing local peak in the electron phase space density at L*~3.8, consistent with local acceleration such as by VLF chorus waves. Overall the simulation results for both storms can accurately reproduce the observed electron flux only when event specific radial diffusion coefficients are used, instead of the empirical diffusion coefficients derived from ULF wave statistics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".