MétaCan
Menu
Back to cohort
Record W1484813406 · doi:10.1063/1.4928387

Ion thermal and dispersion effects in Farley-Buneman instabilities

2015· article· en· W1484813406 on OpenAlexafffund
S. K. Litt, A. I. Smolyakov, Ehab Hassan, W. Horton

Bibliographic record

VenuePhysics of Plasmas · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCollisionalityPhysicsIonAtomic physicsInstabilityDispersion relationPlasmaEquipartition theoremElectronWavelengthMagnetohydrodynamicsHeat fluxKinetic energyWavenumberClassical mechanicsMechanicsCondensed matter physicsHeat transferMagnetic fieldOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Farley-Buneman modes are an example of the collisional instability, which is thought to be the dominant mechanism for the irregularities in low ionosphere region. Despite high collisionality due to electron-neutral and ion-neutral collisions, the kinetic effects associated with finite temperature are important for determination of the mode frequencies and growth rate. This is especially important for ion component that is largely unmagnetized due to low ion cyclotron frequency. The ion thermal effects are strongly pronounced for shorter wavelengths and are crucial for the growth rate cut-off at high wavenumbers. We develop an extended fluid model for ion dynamics to incorporate the effects of ion thermal motion. The model is based on the extended MHD model that includes the evolution equations for higher order moments such as ion viscosity and ion heat flux. We also develop the generalized Chapman-Enskog closure model that provides exact linear closures based on the linearized kinetic equation. The results of these models are compared and tested against the linear kinetic model. The dispersion of Farley-Buneman modes and growth rate behavior are investigated in the short wavelength region.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.445

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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 designTheoretical or conceptual
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

Citations6
Published2015
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of PlasmasSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207