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Record W1891701564 · doi:10.1002/2015ja021346

Electromagnetic ion cyclotron waves in the Earth's magnetosphere with a kappa‐Maxwellian particle distribution

2015· article· en· W1891701564 on OpenAlexafffund
Hajime Sugiyama, S. V. Singh, Yoshiharu Omura, Masafumi Shoji, D. Nunn, Danny Summers

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetospherePhysicsAtomic physicsWhistlerProtonDispersion relationCyclotronElectronAstrophysical plasmaIonComputational physicsPlasmaCharged particleVan Allen ProbesHeliumVan Allen radiation beltNuclear physicsOptics

Abstract

fetched live from OpenAlex

Abstract A theoretical model to study electromagnetic ion cyclotron (EMIC) waves in kappa‐Maxwellian plasma is developed. The plasma is assumed to have five components, i.e., electrons, cool and hot protons, and singly charged helium and oxygen ions. The kappa‐Maxwellian anisotropic particle distribution function is assumed for the hot protons. We use the Kyoto University Plasma Dispersion Analysis Package, a full dispersion solver developed at Kyoto University, to obtain the numerical results and delineate the oxygen, helium, and proton bands. Higher harmonics of the EMIC waves are also studied, and the effects of the kappa distribution on the growth of these waves are clearly demonstrated. Our results are applied to Cluster spacecraft observations of EMIC waves in the inner magnetosphere.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations66
Published2015
Admission routes2
Has abstractyes

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