Global Distribution of ULF Waves During Magnetic Storms: Comparison of Arase, Ground Observations, and BATSRUS + CRCM Simulation
Bibliographic record
Abstract
Abstracts During 26–29 March 2017 magnetic storm, the Arase satellite observed typical ultra low frequency (ULF) waves and acceleration of relativistic electrons. We simulate the global distribution of these ULF waves using CRCM with BATSRUS global magnetospheric magnetohydrodynamic model. The simulation can qualitatively reproduce the ULF waves observed by Arase at frequencies of 2–3 mHz. However, the simulated ULF wave power is 1–2 orders of magnitude smaller than the observation. The simulated ULF wave activity has a good correlation with the solar‐wind dynamic pressure variation, while the wave activity on the ground is enhanced even during the recovery phase, possibly due to the Kelvin‐Helmholtz instability and/or substorms. We also study the 3–6 April 2017 magnetic storm, in which low ULF wave activity and weak acceleration of relativistic electrons are seen. We suggest that the existence of ULF waves plays an important role in accelerating electrons up to relativistic energies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".