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Record W3082441020 · doi:10.1029/2020gl089718

Effects of Polarization Reversal on the Pitch Angle Scattering of Radiation Belt Electrons and Ring Current Protons by EMIC Waves

2020· article· en· W3082441020 on OpenAlexafffund
Xing Cao, Binbin Ni, Danny Summers, Yuri Shprits, Yuequn Lou

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNational Postdoctoral Program for Innovative TalentsChina Postdoctoral Science FoundationChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaChina National Space Administration
KeywordsPhysicsElectronVan Allen radiation beltScatteringPolarization (electrochemistry)Pitch angleAtomic physicsRing currentDiffusionEmic and eticRadiationIonComputational physicsNuclear physicsOpticsPlasmaMagnetosphereGeophysicsChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, we investigate the effects of polarization reversal of electromagnetic ion cyclotron (EMIC) waves at the crossover frequencies on computations of bounce‐averaged pitch angle diffusion coefficients of radiation belt electrons and ring current protons. We find that inclusion of polarization reversal can cause significant changes of H + band‐induced particle diffusion coefficients, while scattering by the He + band is almost unaffected. Our results show that the pure L‐mode approach, which has been widely implemented in previous studies, tends to underestimate the diffusion coefficients of ultrarelativistic (>4 MeV) electrons and overestimate those of 10–50 and >100 keV protons caused by H + band EMIC waves. Especially for >100 keV protons, the differences in diffusion coefficients can be larger by an order of magnitude. We confirm that the polarization reversal can contribute importantly to the scattering loss of radiation belt electrons and ring current protons by H + band EMIC waves.

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 score0.032
Threshold uncertainty score0.313

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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

Citations33
Published2020
Admission routes2
Has abstractyes

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