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Record W4407351441 · doi:10.1029/2024gl111670

Poloidal Field Line Resonances Driven by a Fast Wave

2025· article· en· W4407351441 on OpenAlexafffund
Andrew N. Wright, A. W. Degeling, I. R. Mann, L. G. Ozeke, T. K. Yeoman, J. K. Sandhu, Kazue Takahashi

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Alberta
FundersNatural Environment Research CouncilScience and Technology Facilities CouncilCanadian Space AgencyNational Natural Science Foundation of ChinaNational Aeronautics and Space Administration
KeywordsField (mathematics)PhysicsLine (geometry)GeophysicsField lineQuantum electrodynamicsComputational physicsGeologyPlasmaNuclear physicsMathematics

Abstract

fetched live from OpenAlex

Abstract We present numerical simulations of the excitation of resonant poloidal Alfvén waves. The resulting Alfvén waves could be loosely described as “high‐” ( is the azimuthal wave number) in as much as the azimuthal scale of the wave is much less than the scale in the direction normal to ‐shells. Such waves are generally excited by wave‐particle interactions. In this article we show how resonant poloidal Alfvén waves can be excited by a fast mode (of large azimuthal scale) in a cold plasma. The key property that enables this is a three‐dimensional equilibrium, which facilitates the process of phasemixing in the azimuthal direction. We show that the classification of resonant Alfvén waves as high‐ and low‐ has limited applicability in 3D inhomogeneous media and suggest an alternative classification be based on the excitation mechanism.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.323
Teacher spread0.303 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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