On an energy‐latitude dispersion pattern of ion precipitation potentially associated with magnetospheric EMIC waves
Bibliographic record
Abstract
Abstract Ion precipitation mechanisms are usually energy dependent and contingent upon magnetospheric/ionospheric locations. Therefore, the pattern of energy‐latitude dependence of ion precipitation boundaries seen by low Earth orbit satellites can be implicative of the mechanism(s) underlying the precipitation. The pitch angle scattering of ions led by the field line curvature, a well‐recognized mechanism of ion precipitation in the central plasma sheet (CPS), leads to one common pattern of energy‐latitude dispersion, in that the ion precipitation flux diminishes at higher (lower) latitudes for protons with lower (higher) energies. In this study, we introduce one other systematically existing pattern of energy‐latitude dispersion of ion precipitation, in that the lower energy ion precipitation extends to lower latitude than the higher‐energy ion precipitation. Via investigating such a “reversed” energy‐latitude dispersion pattern, we explore possible mechanisms of ion precipitation other than the field line curvature scattering. We demonstrate via theories and simulations that the H‐band electromagnetic ion cyclotron (EMIC) wave is capable of preferentially scattering keV protons in the CPS and potentially leads to the reversed energy‐latitude dispersion of proton precipitation. We then present detailed event analyses and provide support to a linkage between the EMIC waves in the equatorial CPS and ion precipitation events with reversed energy‐latitude dispersion. We also discuss the role of ion acceleration in the topside ionosphere which, together with the CPS ion population, may result in a variety of energy‐latitude distributions of the overall ion precipitation.
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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".