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Record W2781534925 · doi:10.1063/1.4996719

Marginal stability, characteristic frequencies, and growth rates of gradient drift modes in partially magnetized plasmas with finite electron temperature

2018· article· en· W2781534925 on OpenAlexaff
V. P. Lakhin, V. I. Ilgisonis, A. I. Smolyakov, E. A. Sorokina, N. A. Marusov

Bibliographic record

VenuePhysics of Plasmas · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
FundersRussian Science FoundationMinistry of Education and Science of the Russian Federation
KeywordsPhysicsGyroradiusInstabilityPlasmaTwo-stream instabilityMarginal stabilityMagnetic fieldPlasma oscillationDiamagnetismAtomic physicsElectronElectric fieldRADIUSElectron temperatureWavelengthCollision frequencyCondensed matter physicsComputational physicsMechanicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The detailed analysis of stability of azimuthal oscillations in partially magnetized plasmas with crossed electric and magnetic fields is presented. The instabilities are driven by the transverse electron current which, in general, is due to a combination of E×B and electron diamagnetic drifts. Marginal stability boundary is determined for a wide range of the equilibrium plasma parameters. It is shown that in some regimes near the instability threshold, only the low-frequency long-wavelength oscillations are unstable, while the short-wavelength high-frequency modes are stabilized by the finite Larmor radius effects. Without such stabilization, the high-frequency modes have much larger growth rates and dominate. A new regime of the instability driven exclusively by the magnetic field gradient is identified. Such instability takes place in the region of the weak electric field and for relatively large gradients of plasma density (ρs/ln>1, where ρs is the ion-sound Larmor radius and ln is the scale length of plasma density inhomogeneity).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.207
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2018
Admission routes1
Has abstractyes

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