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Record W4200312930 · doi:10.1063/5.0061485

Field line random walk in magnetic turbulence

2021· article· en· W4200312930 on OpenAlexafffund
A. Shalchi

Bibliographic record

VenuePhysics of Plasmas · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsTurbulenceStatistical physicsRandom walkK-epsilon turbulence modelK-omega turbulence modelField lineField (mathematics)DiffusionMagnetic fieldLine (geometry)Classical mechanicsMechanicsQuantum mechanicsMathematicsStatisticsGeometry

Abstract

fetched live from OpenAlex

The stochastic behavior of magnetic field lines in turbulence is explored analytically and numerically. This problem is a fundamental aspect of turbulence research but also highly relevant in the theory of energetic particles. In the current paper, previous approaches are reviewed and some simple heuristic arguments are provided helping the reader to understand the reason for the form of analytical results. The importance of the so-called Kubo number in field line random walk theory is also discussed. Furthermore, analytical results for a position-dependent field line diffusion coefficient are provided. For more realistic turbulence configurations, the field line diffusion coefficients are computed numerically. This includes quasi-slab, quasi-2D, two-component, and three-dimensional turbulence. Specific aspects of the field line random walk in each model are also discussed. Results based on a diffusion approximation are compared with numerical results obtained without employing this approximation with the aim to explore its validity and accuracy. Numerical results based on simulations for incompressible and compressible turbulence are also discussed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.529

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 integrity0.0000.000
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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 designObservational
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

Citations16
Published2021
Admission routes2
Has abstractyes

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