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Record W4317606122 · doi:10.1029/2022gl102394

Resonant Scattering of Radiation Belt Electrons at Saturn by Ion Cyclotron Waves

2023· article· en· W4317606122 on OpenAlexafffund
Xing Cao, Peng Lü, Binbin Ni, Danny Summers, Yuri Shprits, Minyi Long, Xiaoyu Wang

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMemorial University of Newfoundland
FundersNational Key Research and Development Program of ChinaFundamental Research Funds for the Central UniversitiesChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSaturnPhysicsElectronVan Allen radiation beltCyclotronScatteringPitch angleAtomic physicsIonElectron precipitationKinetic energyMagnetosphereComputational physicsNuclear physicsOpticsGeophysicsAstrophysicsPlasma

Abstract

fetched live from OpenAlex

Abstract By constructing an empirical model of the spectral and latitudinal distribution of ion cyclotron waves on the basis of Cassini datasets, we investigate the resonant interactions between ion cyclotron waves and radiation belt electrons at Saturn. Calculations based on quasi‐linear bounce‐averaged diffusion coefficients show that at Saturn ion cyclotron waves can efficiently pitch angle scatter >∼1 MeV to tens of MeV electrons into the loss cone thereby inducing precipitation loss, while the mixed and momentum scattering effects are typically negligible. The resultant electron loss timescales range from a few to tens of minutes, which in fact decrease significantly with increasing L ‐shell at L = 4–6. We also find that the kinetic effects introduced by pick‐up ring particles cause distinct changes in pitch angle scattering efficiency for lower energy electrons (<3 MeV at L = 5). Our results demonstrate that ion cyclotron waves play a significant role in the dynamics of Saturn's radiation belt electrons.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.287
Teacher spread0.269 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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