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Record W3112202407 · doi:10.1029/2020gl091613

On the Origin of Donut‐Shaped Electron Distributions Within Magnetic Cavities

2020· article· en· W3112202407 on OpenAlexaff
Jing‐Huan Li, Xu‐Zhi Zhou, Qiugang Zong, F. Yang, S. Y. Fu, Shutao Yao, Ji Liu, Quanqi Shi

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsElectronPhysicsPitch angleMagnetic fieldPlasmaBetatronAtomic physicsComputational physicsAnisotropyOpticsGeophysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract Magnetic cavities, also known as magnetic holes, are ubiquitous in space plasmas characterized by depressed magnetic strength and enhanced plasma pressure. Most of the observed cavities are associated with anisotropic particle distributions with higher fluxes in the direction perpendicular to the magnetic field. Recent observations of kinetic‐scale magnetic cavities have identified another type of electron distributions in the pitch angle spectrum, the so‐called donut‐shaped distributions, although their formation mechanism remains unclear. Here, we present a simplistic model of cavity shrinkage and deepening, in which electrons are traced backward in time to the initial, equilibrium‐state cavity. The resulting electron distributions, determined from Liouville's theorem, agree with the observations in the presence of donut‐shaped pitch angle structures. The model also enables a quantitative evaluation on the roles of betatron cooling, radial transport, and pitch angle variations in the formation of donut‐shaped electron distributions within evolving magnetic cavities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0010.001
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 head0.022
GPT teacher head0.281
Teacher spread0.259 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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