Occurrence Rates and Variability of Whistler‐Mode Waves in the Plasma Trough
Bibliographic record
Abstract
Abstract Numerical models of energetic electron behavior in the outer radiation belt require descriptions of the wave‐particle interactions across the inner magnetosphere. Quasilinear diffusion coefficients describe gyro‐resonant wave‐particle interactions over large time‐ and length‐scales but these must be constrained by observations to construct realistic radiation belt models. Recent work indicates the importance of identifying and including realistic spatiotemporal variation of diffusion coefficients. In this paper, we study the spatiotemporal variability of whistler‐mode waves outside the plasmasphere, typically referred to as whistler‐mode chorus. We separately consider the probability of (a) parts of the model domain being outside the plasmasphere, and (b) the probability of detecting wave activity should that part of the model domain be outside the plasmasphere. We discover that the spatiotemporal variability of whistler‐mode waves significantly differs across the model domain; we propose that wave power variability in short wave intervals ( min) is a useful characteristic to distinguish between two types of whistler‐mode waves, especially where their frequency ranges overlap. Our novel spatiotemporal variability analysis indicates that low variability waves are dayside exohiss whose typically high occurrence rate () decreases with substorm activity, and high variability waves are sporadic post‐midnight/dawn sector substorm‐driven chorus with a typical occurrence rate of 0.2. Further, although previous studies often combine the occurrence rates and wave characteristics into climatological averages of chorus wave power, this study highlights the importance of separating the study of occurrence rates and power of the waves, since each can have a different relationship with driving factors.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".