Effects of finite electron temperature on gradient drift instabilities in partially magnetized plasmas
Bibliographic record
Abstract
The gradient-drift instabilities of partially magnetized plasmas in plasma devices with crossed electric and magnetic fields are investigated in the framework of the two-fluid model with finite electron temperature in an inhomogeneous magnetic field. The finite electron Larmor radius (FLR) effects are also included via the gyroviscosity tensor taking into account the magnetic field gradient. This model correctly describes the electron dynamics for k⊥ρe>1 in the sense of Padé approximants (here, k⊥ and ρe are the wavenumber perpendicular to the magnetic field and the electron Larmor radius, respectively). The local dispersion relation for electrostatic plasma perturbations with the frequency in the range between the ion and electron cyclotron frequencies and propagating strictly perpendicular to the magnetic field is derived. The dispersion relation includes the effects of the equilibrium E×B electron current, finite ion velocity, electron inertia, electron FLR, magnetic field gradients, and Debye length effects. The necessary and sufficient condition of stability is derived, and the stability boundary is found. It is shown that, in general, the electron inertia and FLR effects stabilize the short-wavelength perturbations. In some cases, such effects completely suppress the high-frequency short-wavelength modes so that only the long-wavelength low-frequency (with respect to the lower-hybrid frequency) modes remain unstable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".