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Record W2121288654 · doi:10.1109/plasma.2002.1030481

Eigenmodes of an ion plasma sheath

2003· article· en· W2121288654 on OpenAlexaff
F. Detering, A. I. Smolyakov, I. Kh. Khabibrakhmanov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNonlinear Waves and Solitons
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhysicsTransverse planePlasmaEigenvalues and eigenvectorsDispersion relationIonNonlinear systemDebye sheathBoundary value problemPlane (geometry)Plasma oscillationIon acoustic waveAtomic physicsQuantum electrodynamicsClassical mechanicsMechanicsCondensed matter physicsQuantum mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

Summary form only given. We investigate the structure and stability of transverse (in the sheath plane) eigenmodes of a collisionless ion sheath occurring at the plasma boundary. An ion sheath represents a particular example of nonlinear ion-sound waves, which, in general include solitons and periodic waves structures. All these nonlinear waves can be found by formulating plasma equations in a form of the oscillator eigenvalue problem in a generic nonlinear potential (Sagdeev potential). Solitons and periodic waves correspond to trapped (localized) states, while the sheath solution is an untrapped state with the local maximum as the wall. To investigate the transverse stability and eigenmodes of the sheath solution we employ a method previously used to study transverse oscillations of solitons (e.g. within the Kadomtsev-Petviashvili equation). We have generalized this technique for the sheath solution. We have obtained the dispersion relation for transverse oscillations and found stable eigenmodes with a frequency lower than the ion plasma frequency.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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)0.0060.001

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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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 designTheoretical or conceptual
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

Citations0
Published2003
Admission routes1
Has abstractyes

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