On the Relative Strength of Electric and Magnetic ULF Wave Radial Diffusion During the March 2015 Geomagnetic Storm
Bibliographic record
Abstract
Abstract In this paper, we study electron radial diffusion coefficients derived from Pc4‐Pc5 ultralow frequency (ULF) wave power during the intense geomagnetic storm on 17–18 March 2015. During this storm the population of highly relativistic electrons was depleted within 2 hr of the storm commencement. This radial diffusion, depending upon the availability of source populations, can cause outward radial diffusion of particles and their loss to the magnetosheath, or inward transport and acceleration. Analysis of electromagnetic field measurements from Geostationary Operational Environment Satellite (GOES), Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellite, and ground‐based magnetometers shows that the main phase storm‐specific radial diffusion coefficients do not correspond to statistical estimates. Specifically, during the main phase, the electric diffusion ( ) is reduced, and the magnetic diffusion ( ) is increased, compared to empirical models based on Kp. Contrary to prior results, the main phase magnetic radial diffusion cannot be neglected. The largest discrepancies, and periods of dominance of over , occur during intervals of strongly southward IMF. However, during storm recovery, both magnetic and electric diffusion rates are consistent with empirical estimates. We further verify observationally, for the first time, an energy coherence for both and where diffusion coefficients do not depend on energy. We show that, at least for this storm, properly characterizing main phase radial diffusion, potentially associated with enhanced ULF wave magnetopause shadowing losses, cannot be done with standard empirical models. Modifications, associated especially with southward IMF, which enhance the effects of and introduce larger main phase outward transport losses, are needed.
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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 | 0.000 | 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".