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Record W2103526594 · doi:10.5194/angeo-31-451-2013

Ion temperature anisotropy effects on threshold conditions of a shear-modified current driven electrostatic ion-acoustic instability in the topside auroral ionosphere

2013· article· en· W2103526594 on OpenAlexaff
P. Perron, Jean-Marc Noël, К. Кабин

Bibliographic record

VenueAnnales Geophysicae · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of SaskatchewanRoyal Military College of Canada
Fundersnot available
KeywordsGyroradiusInstabilityAnisotropyPhysicsIonElectric fieldIonosphereAtomic physicsDispersion relationPlasmaComputational physicsCondensed matter physicsGeophysicsMechanicsOptics

Abstract

fetched live from OpenAlex

Abstract. Temperature anisotropies may be encountered in space plasmas when there is a preferred direction, for instance, a strong magnetic or electric field. In this paper, we study how ion temperature anisotropy can affect the threshold conditions of a shear-modified current driven electrostatic ion-acoustic (CDEIA) instability. In particular, this communication focuses on instabilities in the context of topside auroral F-region situations and in the limit where finite Larmor radius corrections are small. We derived a new fluid-like expression for the critical drift which depends explicitly on ion anisotropy. More importantly, for ion to electron temperature ratios typical of F-region, solutions of the kinetic dispersion relation show that ion temperature anisotropy may significantly lower the drift threshold required for instability. In some cases, a perpendicular to parallel ion temperature ratio of 2 and may reduce the relative drift required for the onset of instability by a factor of approximately 30, assuming the ion-acoustic speed of the medium remains constant. Therefore, the ion temperature anisotropy should be considered in future studies of ion-acoustic waves and instabilities in the high-latitude ionospheric F-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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.915

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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 designObservational
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

Citations4
Published2013
Admission routes1
Has abstractyes

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