EMIC wave induced proton precipitation during the 27-28 May 2017 storm:Comparison of BATSRUS+RAM-SCB simulations with ground/space based observations
Bibliographic record
Abstract
Recent studies have shown that the ion precipitation induced by EMIC waves can contribute significantly to the total energy flux deposited into the ionosphere and severely affect the magnetosphere-ionosphere coupling. During the geomagnetic storm of 27-28 May 2017, the ARASE and the RBSPa satellites observed typical signatures of EMIC waves in the inner magnetosphere. The DMSP and MetOp satellites observed enhanced proton precipitation during the main phase of the storm. In order to understand the evolution of proton precipitation into the ionosphere, its correspondence to the time and location of excitation of the EMIC waves and its relation to the source and distribution of proton temperature anisotropy, we conducted two simulations of the BATSRUS+RAMSCBE model with and without EMIC waves included. Simulation results suggest that the H- and He-band EMIC waves are excited within regions of strong temperature anisotropy of protons in the vicinity of the plasmapause. In regions where the Arase/RBSPa satellite measurements recorded EMIC wave activity, an increase in the simulated growth rates of H- and He-band EMIC waves is observed indicating that the model is able to capture the EMIC wave activity. The RAM-SCBE simulation with EMIC waves reproduces the precipitating fluxes in the premidnight sector fairly well, and is found to be in good agreement with the DMSP and MetOp satellite observations. The results suggest that the EMIC wave scattering of ring current ions gives rise to the proton precipitation in the premidnight sector at subauroral latitudes during the main phase of the 27 May 2017 storm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".