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Record W2734250686 · doi:10.1063/1.4990054

Non-local Ohm's law during collisions of magnetic flux ropes

2017· article· en· W2734250686 on OpenAlexaff
Walter Gekelman, T. DeHaas, Patrick Pribyl, S. Vincena, B. Van Compernolle, R. D. Sydora

Bibliographic record

VenuePhysics of Plasmas · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsUniversity of Alberta
FundersOffice of the President, University of CaliforniaU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsOhmOhm's lawElectrical resistivity and conductivityMagnetic fieldRopeMagnetic fluxPlasmaFlux (metallurgy)MagnetohydrodynamicsCondensed matter physicsMagnetic reconnectionDissipationQuantum electrodynamicsComputational physicsMechanicsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Two kink unstable magnetic flux ropes are produced in a carefully diagnosed laboratory experiment. Using probes, the time varying magnetic field, plasma potential, plasma flow, temperature, and density were measured at over 42 000 spatial locations. These were used to derive all the terms in Ohm's law to calculate the plasma resistivity. The resistivity calculated by this method was negative in some spatial regions and times. Ohm's law was shown to be non-local. Instead, the Kubo resistivity at the flux rope kink frequency was calculated using the fluctuation dissipation theorem. The resistivity parallel to the magnetic field was as large as 40 times the classical value and peaked where magnetic field line reconnection occurred as well as in the regions of large flux rope current.

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)0.001
metaresearch head score (Gemma)0.003
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
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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