Electron scattering by whistler‐mode ELF hiss in plasmaspheric plumes
Bibliographic record
Abstract
Nonadiabatic loss processes of radiation belt energetic electrons include precipitation loss to the atmosphere due to pitch‐angle scattering by various magnetospheric plasma wave modes. Here we consider electron precipitation loss due to pitch‐angle scattering by whistler‐mode ELF hiss in plasmaspheric plumes. Using wave observations and inferred plasma densities from the Plasma Wave Experiment on the Combined Release and Radiation Effects Satellite (CRRES), we analyze plume intervals for which well‐determined hiss spectral intensities are available. We then select 14 representative plumes for detailed study, comprising 10 duskside plumes and 4 nonduskside plumes, with local hiss amplitudes ranging from maximum values of above 300 pT to minimum values of less than 1 pT. We estimate the electron loss timescale τloss due to pitch‐angle scattering by hiss in each chosen plume as a function of L‐shell and electron energy; τloss is calculated from quasi‐linear theory as the inverse of the bounce‐averaged diffusion rate evaluated at the equatorial loss cone angle. We find that pitch‐angle scattering by hiss in plumes can be efficient for inducing precipitation loss of outer‐zone electrons with energies throughout the range 100 keV to 1 MeV, though the magnitude of τloss can be highly dependent on wave power, L‐shell, and electron energy. For 100‐ to 200‐keV electrons, typically τloss ∼ 1 day while the minimum loss timescale (τloss)min ∼ hours. For 500‐keV to 1‐MeV electrons, typically (τloss)min ∼ days, while (τloss)min < 1 day in the case of large wave amplitude (∼100's pT). Apart from inducing direct precipitation loss of MeV electrons, scattering by hiss in plumes may reduce the generation of MeV electrons by depleting the lower energy electron seed population. Models of the dynamical variation of the outer‐zone electron flux should incorporate electron precipitation loss induced by ELF hiss scattering in plasmaspheric plumes.
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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 | 0.000 |
| 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".