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Record W4322489451 · doi:10.1029/2022ja031163

Characteristics of Electron Precipitation Directly Driven by Poloidal ULF Waves

2023· article· en· W4322489451 on OpenAlexafffund
Ze‐Fan Yin, Xu‐Zhi Zhou, Wen Li, Xiaochen Shen, R. Rankin, Ji Liu, Zejun Hu, Jianjun Liu, Qiugang Zong, Li Li, Yong‐Fu Wang

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanadian Space AgencyNational Science Foundation
KeywordsElectron precipitationElectronPhysicsMagnetospherePrecipitationComputational physicsAtomic physicsAtmosphere (unit)Excited stateDipoleMagnetic fieldGeophysicsMeteorologyNuclear physics

Abstract

fetched live from OpenAlex

Abstract A mechanism recently proposed for magnetospheric electron loss into the atmosphere is the precipitation directly driven by ultralow‐frequency (ULF) waves. In this study, we quantitatively analyze the properties of ULF wave‐induced precipitation by simulating the electron bounce and drift motion in poloidal‐mode waves excited in a dipole magnetic field. Our results reveal that precipitation occurs only when electrons encounter a westward‐directed wave electric field in the magnetosphere, which leads to cross‐field energy enhancements and reduces their mirror heights. The simulations also demonstrate longer duration electron precipitation at the drift‐resonance energy. We calculate the temporal variations of the energy spectrum for precipitating electrons and the total precipitating energy fluxes. These results improve our understanding of ULF wave‐induced electron precipitation as well as provide a point of comparison for observations from balloons or ground‐based instruments.

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)0.001
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 score0.199
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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