Understanding Quiet and Storm Time EMIC Waves—Van Allen Probes Results
Bibliographic record
Abstract
Abstract Geomagnetic indices have been used as a proxy for studying electromagnetic ion cyclotron (EMIC) wave occurrences under different geomagnetic conditions. However, the drivers of EMIC waves are different during non‐storm, storm time and during individual storm phases. Using ∼7 years of data from the twin Van Allen Probes, we demonstrate that the occurrence probability of EMIC waves are not well captured by a specific geomagnetic activity index alone, but is rather well manifested by considering individual storm phases. We show EMIC wave occurrence statistics during different storm phases (pre‐onset, main and recovery) for geomagnetic activity indices Sym‐H, AE, and Kp and solar wind dynamic pressure Pdyn, illustrating that the occurrence rates vary significantly during different storm phases even for a given geomagnetic index. We also utilize this large database to show EMIC wave occurrence distribution, and how various wave and plasma parameters behave under different geomagnetic conditions. EMIC waves occur 2.9 times more often during geomagnetic storms than during non‐storm times. The majority (72%) of storm time EMIC waves occur during the recovery phase due to long recovering time, while the highest occurrence rates are in the pre‐onset phase, followed by main and recovery phases. EMIC waves in the main phase have occurrence peaks in the dusk to pre‐midnight sectors while recovery phase events spread to more Magnetic Local Time (MLT) sectors with peaks in the morning sector. Wave amplitudes are found to be evenly distributed across different MLT sectors during all geomagnetic conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".