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Record W2728497411 · doi:10.1063/1.4989712

Dust kinetic Alfvén solitary and rogue waves in a dusty plasma with two temperature nonextensive ions

2017· article· en· W2728497411 on OpenAlexaff
N. S. Saini, Barjinder Kaur, Manpreet Singh, A. S. Bains

Bibliographic record

VenuePhysics of Plasmas · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsUniversity of Saskatchewan
FundersUniversity Grants Commission
KeywordsPhysicsDusty plasmaIonKinetic energyPlasmaElectron temperatureAtomic physicsElectronAmplitudeNonlinear systemPerturbation (astronomy)Rogue wavePlasma parametersClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

A theoretical investigation is carried out to study small amplitude dust kinetic Alfvén solitary waves (DKASWs) and rogue waves in a low-β, electron depleted plasma consisting of negatively charged dust grains and two temperature ions which are modelled by q-nonextensive distribution. A nonlinear Korteweg-de Vries equation, which governs the evolution of DKASWs, has been derived using the reductive perturbation method. Combined effects of the nonextensivity of ions, plasma beta, temperature ratio of low and high temperature ions, concentration of ions as well as dust, and angle of propagation (θ) have been studied in detail on the propagation properties of DKASWs. Only negative potential Alfvénic solitary waves are observed in the present study. Further, the study is extended for dust kinetic Alfvén rogue wave (DKARW) solutions. The properties of DKARWs, influenced by plasma parameters in question, are discussed in detail. The findings of this study may be useful to understand the formation of nonlinear coherent structures in Saturn's F-ring.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 communication0.0000.001
Open science0.0000.001
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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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