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Record W2909737041 · doi:10.1029/2018gl081550

Sensitivity of EMIC Wave‐Driven Scattering Loss of Ring Current Protons to Wave Normal Angle Distribution

2019· article· en· W2909737041 on OpenAlexafffund
Xing Cao, Binbin Ni, Danny Summers, Yuri Shprits, Xudong Gu, Song Fu, Yuequn Lou, Yang Zhang, Xin Ma, Wenxun Zhang, H. Huang, Juan Yi

Bibliographic record

VenueGeophysical Research Letters · 2019
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 China
KeywordsScatteringCyclotronRing currentPhysicsPitch angleAtomic physicsComputational physicsIonMagnetic fieldOpticsMagnetosphere

Abstract

fetched live from OpenAlex

Abstract Electromagnetic ion cyclotron waves have long been recognized to play a crucial role in the dynamic loss of ring current protons. While the field‐aligned propagation approximation of electromagnetic ion cyclotron waves was widely used to quantify the scattering loss of ring current protons, in this study, we find that the wave normal distribution strongly affects the pitch angle scattering efficiency of protons. Increase of peak normal angle or angular width can considerably reduce the scattering rates of ≤ 10 keV protons. For >10 keV protons, the field‐aligned propagation approximation results in a pronounced underestimate of the scattering of intermediate equatorial pitch angle protons and overestimates the scattering of high equatorial pitch angle protons by orders of magnitude. Our results suggest that the wave normal distribution of electromagnetic ion cyclotron waves plays an important role in the pitch angle evolution and scattering loss of ring current protons and should be incorporated in future global modeling of ring current dynamics.

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 score0.472
Threshold uncertainty score0.588

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

Citations55
Published2019
Admission routes2
Has abstractyes

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