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Record W2021529845 · doi:10.1063/1.3469578

Random walk of magnetic field lines in dynamical turbulence: A field line tracing method. I. Slab turbulence

2010· article· en· W2021529845 on OpenAlexaff
A. Shalchi

Bibliographic record

VenuePhysics of Plasmas · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhysicsTurbulenceStatistical physicsK-omega turbulence modelK-epsilon turbulence modelField lineField (mathematics)Magnetic fieldRandom walkClassical mechanicsComputational physicsMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

To study the wandering of magnetic field lines is an important subject in theoretical physics. Results of field line random walk theories can be applied in plasma physics as well as astrophysics. Previous investigations are based on magnetostatic models. These models have been used in analytical work as well as in computer simulations to warrant mathematical and numerical tractability. To replace the magnetostatic model by a dynamical turbulence model is a difficult task. In the present article, a field line tracing method is used to describe field line wandering in dynamical magnetic turbulence. As examples different models are employed, namely, the plasma wave model, the damping model of dynamical turbulence, and the random sweeping model. It is demonstrated that the choice of the turbulence model has a very strong influence on the field line structure. It seems that if dynamical turbulence effects are included, Markovian diffusion can be found for other forms of the wave spectrum as in the magnetostatic model. Therefore, the results of the present paper are useful to specify turbulence models. As a further application we consider charged particle transport at early times.

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 score0.658
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.264
Teacher spread0.257 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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