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Record W2135664314 · doi:10.5194/angeo-32-889-2014

Effects of cold electron number density variation on whistler-mode wave growth

2014· article· en· W2135664314 on OpenAlexafffund
Rongxin Tang, Danny Summers, Xiaohua Deng

Bibliographic record

VenueAnnales Geophysicae · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNational Science Foundation
KeywordsWhistlerPhysicsElectron densityElectronWavenumberAmplitudePlasmaNonlinear systemGrowth rateComputational physicsAtomic physicsOpticsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

Abstract. We examine how the growth of magnetospheric whistler-mode waves depends on the cold (background) electron number density N0. The analysis is carried out by varying the cold-plasma parameter a = (electron gyrofrequency)2/(electron plasma frequency)2 which is proportional to 1/N0. For given values of the thermal anisotropy AT and the ratio Nh/N0, where Nh is the hot (energetic) electron number density, we find that, as N0 decreases, the maximum values of the linear and nonlinear growth rates decrease and the threshold wave amplitude for nonlinear growth increases. Generally, as N0 decreases, the region of (Nh/N0, AT)-parameter space in which nonlinear wave growth can occur becomes more limited; that is, as N0 decreases, the parameter region permitting nonlinear wave growth shifts to the top right of (Nh/N0, AT) space characterized by larger Nh/N0 values and larger AT values. The results have implications for choosing input parameters for full-scale particle simulations and also in the analysis of whistler-mode chorus data.

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 score0.728
Threshold uncertainty score0.754

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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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

Citations15
Published2014
Admission routes2
Has abstractyes

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