Effects of cold electron number density variation on whistler-mode wave growth
Bibliographic record
Abstract
We examine how the growth of magnetospheric whistler-mode waves depends on the cold (background) electron number density N-0. The analysis is carried out by varying the cold-plasma parameter a = (electron gyrofrequency)(2)/(electron plasma frequency) 2 which is proportional to 1/N-0. For given values of the thermal anisotropy A(T) and the ratio N-h/N-0, where N-h is the hot (energetic) electron number density, we find that, as N-0 decreases, the maximum values of the linear and nonlinear growth rates decrease and the threshold wave amplitude for nonlinear growth increases. Generally, as N-0 decreases, the region of (N-h/N-0, A(T))-parameter space in which nonlinear wave growth can occur becomes more limited; that is, as N-0 decreases, the parameter region permitting nonlinear wave growth shifts to the top right of (N-h/N-0, A(T)) space characterized by larger N-h/N-0 values and larger A(T) values. The results have implications for choosing input parameters for full-scale particle simulations and also in the analysis of whistler-mode chorus data.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".