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Record W1964440139 · doi:10.1134/s0021364010110044

Dust acoustic waves in a nonequilibrium dusty plasma

2010· article· en· W1964440139 on OpenAlexaff
А. В. Филиппов, А. Н. Старостин, I. M. Tkachenko, В. Е. Фортов, D. Ballester, Luis Conde

Bibliographic record

VenueJournal of Experimental and Theoretical Physics Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsQueen's University
Fundersnot available
KeywordsDusty plasmaPlasmaPhysicsNon-equilibrium thermodynamicsElectronDebye lengthWavelengthDispersion relationDebyeIonAtomic physicsCondensed matter physicsThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

With the use of the method of moments applicable for any values of the parameter of the nonideality of a dusty plasma and the hydrodynamic approach applicable only for small nonideality parameters, the theory of waves and oscillations of a complex plasma has been generalized to the case of a two-exponential interaction potential. It has been shown that the hydrodynamic approach and method of moments give the same dispersion relation for small nonideality parameters. It has been demonstrated that the velocity of dust acoustic waves in the long- and short-wavelength regions is determined by the small and large screening constants, respectively. It has been shown that the velocity of dust acoustic waves in nonequilibrium plasma is much higher than that obtained in the Debye screening theory for equilibrium plasma. In the hydrodynamic approach, the importance of the inclusion of the self-consistent mutual effect of the dust, electron, and ion components, and sinks of electrons and ions on dust particles, which lead to a noticeable change in the parameters of the interaction potential of dust particles, has been demonstrated.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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