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Record W3097579815 · doi:10.1038/s41467-020-19442-0

Self-consistent kinetic model of nested electron- and ion-scale magnetic cavities in space plasmas

2020· article· en· W3097579815 on OpenAlexafffund
Jing‐Huan Li, F. Yang, Xu‐Zhi Zhou, Qiugang Zong, Anton Artemyev, R. Rankin, Quanqi Shi, Shutao Yao, Han Liu, Jiansen He, Z. Y. Pu, Chijie Xiao, Liu Ji, C. J. Pollock, G. Le, J. L. Burch

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersCanadian Space AgencyPeking UniversityRussian Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhysicsPlasmaSpacecraftKinetic energyElectronAstrophysical plasmaComputational physicsAtomic physicsIonMagnetic fieldGyrokineticsProtonCascadeNuclear physicsClassical mechanicsTokamakQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

NASA's Magnetospheric Multi-Scale (MMS) mission is designed to explore the proton- and electron-gyroscale kinetics of plasma turbulence where the bulk of particle acceleration and heating takes place. Understanding the nature of cross-scale structures ubiquitous as magnetic cavities is important to assess the energy partition, cascade and conversion in the plasma universe. Here, we present theoretical insight into magnetic cavities by deriving a self-consistent, kinetic theory of these coherent structures. By taking advantage of the multipoint measurements from the MMS constellation, we demonstrate that our kinetic model can utilize magnetic cavity observations by one MMS spacecraft to predict measurements from a second/third spacecraft. The methodology of "observe and predict" validates the theory we have derived, and confirms that nested magnetic cavities are self-organized plasma structures supported by trapped proton and electron populations in analogous to the classical theta-pinches in laboratory plasmas.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.508

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 integrity0.0000.001
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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 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

Citations28
Published2020
Admission routes2
Has abstractyes

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