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Record W3081141084 · doi:10.3847/2041-8213/abab92

Electron Energization and Energy Dissipation in Microscale Electromagnetic Environments

2020· article· en· W3081141084 on OpenAlexaff
Jingao Liu, Shutao Yao, Quanqi Shi, X. G. Wang, Qiugang Zong, Yanying Feng, Han Liu, Ruilong Guo, Zhonghua Yao, I. J. Rae, A. W. Degeling, Anmin Tian, C. T. Russell, Y. T. Zhang, Y. X. Wang, L. D. Woodham, Z. Y. Pu, Chijie Xiao, S. Y. Fu, B. L. Giles

Bibliographic record

VenueThe Astrophysical Journal Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNatural Environment Research CouncilEuropean CommissionSight Research UKShandong University
KeywordsPhysicsMicroscale chemistryDissipationSpacecraftAdiabatic processElectronComputational physicsClassical mechanicsPlasmaMechanicsAstrophysicsAstronomyQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Particle energization and energy dissipation in electromagnetic environments are longstanding topics of intensive research in space, laboratory, and astrophysical plasmas. One challenge is to understand these conversion processes at smaller and smaller spatial/temporal scales. In this Letter, with very high cadence measurements of particle distributions from the Magnetospheric Multiscale spacecraft, we report evidence of evolution of an identified microscale (i.e., electron gyro-scale) magnetic cavity structure and reveal within it a unique energization process that does not adhere to prevailing adiabatic invariance theory. Our finding indicates that this process is largely energy dependent, and can accelerate/decelerate charged particles inside the trapping region during their gyromotion, clearly altering the particle distribution.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.175
Teacher spread0.173 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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