MétaCan
Menu
Back to cohort
Record W1997896847 · doi:10.1139/p08-021

Large-amplitude dust-acoustic solitary waves in an electron-depleted hot dusty plasma with trapped ions

2008· article· en· W1997896847 on OpenAlexvenueno aff
Mouloud Tribeche, Smain Younsi, Taha Houssine Zerguini

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsDusty plasmaIonPlasmaAmplitudeAtomic physicsElectronNonlinear systemElectron temperatureMach numberMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

A theoretical model is presented to show the existence, formation, and possible realization of large-amplitude dust-acoustic solitary potentials in electron-depleted hot dusty plasma with trapped ions. These nonlinear localized structures are self-consistent solutions of the Vlasov equation in which the ion response is non-Maxwellian due to the ion trapping in the large-amplitude plasma potentials. Emphasis is given to the role of the grain temperature. Interestingly, one finds that the effect of increasing the dust temperature is to restrict the domain of allowable Mach numbers. The latter enlarges when the relative amount of trapped ions is increased. Furthermore, the nonlinear potential shrinks and exhibits smaller depth and smaller width than the one involving cold dust grains. The strong localization of the dust particles becomes less pronounced when the dust temperature is increased. The results complement and provide new insights into previously published results on this problem and their relevance to space dusty plasmas is pointed out.PACS Nos.: 52.27.Lw; 52.35.Fp; 52.35.Sb; 52.35.Tc; 52.35.Mw

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicDust and Plasma Wave PhenomenaFrench-language works237,207