Bibliographic record
Abstract
We introduce a new particle distribution function in plasma physics called the subtracted-kappa (S-K) distribution. This new distribution incorporates a loss-cone feature and an enhanced high-energy tail characterized by the spectral index κ. The S-K distribution is a generalization of the well-known subtracted-Maxwellian and approaches the latter distribution in the limit as κ→∞. Further, the S-K distribution usefully includes the bi-kappa and kappa-loss-cone distributions as special cases. Two sources of free plasma energy are provided by the S-K distribution, namely, the loss cone property and the thermal anisotropy. Both free energy sources can excite wave growth. In this paper, we briefly consider the influence of the S-K distribution on three wave phenomena: (a) linear growth of whistler-mode waves generated by an injection of hot electrons into a cold plasma, (b) dispersion of R-mode and L-mode electromagnetic waves in a hot plasma, and (c) transition from linear to nonlinear growth of electromagnetic waves as determined by a critical boundary in the input-parameter space. There are many possibilities for future projects involving the S-K distribution. Charged particle distributions in space typically possess a pronounced high-energy tail that can be modeled approximately by a kappa distribution, and so the S-K distribution is an ideal tool for analyzing kinetic waves and microinstabilities in space plasmas.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".