Ultralow-frequency modulation of whistler-mode wave growth
Bibliographic record
Abstract
[1] Measurements from ground-based magnetometers and riometers at auroral latitudes have demonstrated that energetic (∼30–300 keV) electron precipitation can be modulated in the presence of magnetic field oscillations at ultralow frequencies. It has previously been proposed that an ultralow-frequency (ULF) wave would modulate field and plasma properties near the equatorial plane, thus modifying the growth rates of whistler-mode waves. In turn, the resulting whistler-mode waves would mediate the pitch angle scattering of electrons resulting in ionospheric precipitation. In this paper, we investigate this hypothesis by quantifying the changes to the linear growth rate expected due to a slow change in the local magnetic field strength for parameters typical of the equatorial region around 6.6RE radial distance. To constrain our study, we determine the largest possible ULF wave amplitudes from measurements of the magnetic field at geosynchronous orbit. Using nearly ten years of observations from two satellites, we demonstrate that the variation in magnetic field strength due to oscillations at 2 mHz does not exceed ±10% of the background field. Modifications to the plasma density and temperature anisotropy are estimated using idealized models. For low temperature anisotropy, there is little change in the whistler-mode growth rates even for the largest ULF wave amplitude. Only for large temperature anisotropies can whistler-mode growth rates be modulated sufficiently to account for the changes in electron precipitation measured by riometers at auroral latitudes.
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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".