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Record W1612165395 · doi:10.1063/1.4928891

Modulational instability of ion-acoustic waves in plasma with a q-nonextensive nonthermal electron velocity distribution

2015· article· en· W1612165395 on OpenAlexaff
Omar Bouzit, Mouloud Tribeche, A. S. Bains

Bibliographic record

VenuePhysics of Plasmas · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsModulational instabilityPhysicsInstabilityPlasmaElectronAtomic physicsIonPerturbation (astronomy)Two-stream instabilityPlasma parametersModulation (music)Quantum electrodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Modulation instability of ion-acoustic waves (IAWs) is investigated in a collisionless unmagnetized one dimensional plasma, containing positive ions and electrons following the mixed nonextensive nonthermal distribution [Tribeche et al., Phys. Rev. E 85, 037401 (2012)]. Using the reductive perturbation technique, a nonlinear Schrödinger equation which governs the modulation instability of the IAWs is obtained. Valid range of plasma parameters has been fixed and their effects on the modulational instability discussed in detail. We find that the plasma supports both bright and dark solutions. The valid domain for the wave number k where instabilities set in varies with both nonextensive parameter q as well as non thermal parameter α. Moreover, the analysis is extended for the rational solutions of IAWs in the instability regime. Present study is useful for the understanding of IAWs in the region where such mixed distribution may exist.

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 score0.000
Threshold uncertainty score0.001

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 communication0.0000.000
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 head0.016
GPT teacher head0.230
Teacher spread0.213 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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