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Record W4310365633 · doi:10.1063/5.0104283

Magnetized plasma pressure filaments: Analysis of chaotic and intermittent transport events driven by drift-Alfvén modes

2022· article· en· W4310365633 on OpenAlexafffund
Scott Karbashewski, R. D. Sydora, B. Van Compernolle, T. Simala-Grant, M. J. Poulos

Bibliographic record

VenuePhysics of Plasmas · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsProtein filamentTransverse planePlasmaTurbulenceExponential functionPressure gradientGaussianMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

The origin of intermittent fluctuations in an experiment involving several interacting electron plasma pressure filaments in close proximity, embedded in a large linear magnetized plasma device, is investigated. The probability density functions of the fluctuations on the inner and outer gradient of the filament bundle are non-Gaussian and the time series contain uncorrelated Lorentzian pulses that give the frequency power spectral densities an exponential shape. A cross-conditionally averaged spatial reconstruction of a temporal event reveals that the intermittent character is caused by radially and azimuthally propagating turbulent structures with transverse spatial scales on the order of the electron skin depth. These eruption events originate from interacting pressure gradient-driven drift-Alfvén instabilities on the outer gradient and edge of the filament bundle. The temporal Lorentzian shape of the intermittent structures and exponential spectra are suggestive of deterministic chaos in the underlying dynamics; this conclusion is supported by the complexity–entropy analysis (CH-plane) that shows the experimental time series are located in the chaotic regime.

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 categoriesInsufficient payload (model declined to judge)
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 score0.729
Threshold uncertainty score0.981

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)0.0200.000

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 head0.237
Teacher spread0.228 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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