Accurately specifying storm‐time ULF wave radial diffusion in the radiation belts
Bibliographic record
Abstract
Abstract Ultralow frequency (ULF) waves can contribute to the transport, acceleration, and loss of electrons in the radiation belts through inward and outward diffusion. However, the most appropriate parameters to use to specify the ULF wave diffusion rates are unknown. Empirical representations of diffusion coefficients often use Kp; however, specifications using ULF wave power offer an improved physics‐based approach. We use 11 years of ground‐based magnetometer array measurements to statistically parameterize the ULF wave power with Kp, solar wind speed, solar wind dynamic pressure, and Dst. We find that Kp is the best single parameter to specify the statistical ULF wave power driving radial diffusion. Significantly, remarkable high energy tails exist in the ULF wave power distributions when expressed as a function of Dst. Two‐parameter ULF wave power specifications using Dst as well as Kp provide a better statistical representation of storm‐time radial diffusion than any single variable alone.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".