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Record W2139110956 · doi:10.1017/s0022377801001489

Propagation of surface waves in two-plasma systems bounded by a metallic enclosure

2001· article· en· W2139110956 on OpenAlexafffund
Luc Stafford, J. Margot, T. W. Johnston

Bibliographic record

VenueJournal of Plasma Physics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsPlasmaWavenumberElectron cyclotron resonanceSurface waveElectromagnetic electron waveWave propagationAttenuationMagnetic fieldWaves in plasmasResonance (particle physics)Atomic physicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

This paper examines the possibility of propagating surface waves in cylindrical plasma–plasma structures enclosed by metal walls and submitted or not to a static magnetic field. We consider the situation in which the inner plasma layer is overdense while the other is underdense. It is shown that outside the electron cyclotron resonance (ECR) conditions, the outer plasma layer plays a role similar to that of an ordinary dielectric layer, just modifying the wavenumber without drastically changing the general characteristics of the wave. At ECR, a major change in the wavenumber and attenuation coefficient is observed, a cutoff occurring on the left side of ECR and a resonance on the right side, provided the outer plasma density is large enough. It is further found that in conditions where the outer plasma layer thickness is very small, wave propagation still occurs, whatever the density value in this region. This suggests that surface wave propagation is possible in plasma–sheath–metal structures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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

Citations7
Published2001
Admission routes2
Has abstractyes

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