MétaCan
Menu
Back to cohort
Record W4320923243 · doi:10.1063/5.0129591

The electron diffusion region dominated by electromagnetic turbulence in the reconnection current layer

2023· article· en· W4320923243 on OpenAlexafffund
Keizo Fujimoto, R. D. Sydora

Bibliographic record

VenuePhysics of Plasmas · 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 China
KeywordsPhysicsMagnetic reconnectionTurbulenceDiffusionTurbulence modelingElectrical resistivity and conductivityMomentum diffusionDissipationElectronCurrent sheetOhm's lawViscosityMechanicsMagnetohydrodynamicsPlasmaMagnetic fieldClassical mechanicsThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Most of the plasma fluid equations have employed the electrical resistivity to generate the magnetic dissipation required for magnetic reconnection to occur in collisionless plasma. However, there has been no clear evidence that such a model is indeed appropriate in the reconnection diffusion region in terms of the kinetic physics. The present study demonstrates that, using a large-scale 3D kinetic simulation and analytical analysis, the spatial distribution of the non-ideal electric field is consistent with the dissipation due to the viscosity rather than the resistivity, when electromagnetic (EM) turbulence is dominant in the electron diffusion region (EDR). The effective viscosity is caused by the EM turbulence that is driven by the flow shear instabilities leading to the electron momentum transport across the EDR. The result suggests a fundamental modification of the fluid equations using the resistivity in the Ohm's law. In contrast, for the 2D current sheet without significant turbulence activity, the non-ideal field profile does not obey the simple form based on the viscosity, so that further investigation is needed for a better description.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.406

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations11
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of PlasmasSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207