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Record W2807299664 · doi:10.1063/1.5029322

Electrostatic shock structures in dissipative multi-ion dusty plasmas

2018· article· en· W2807299664 on OpenAlexfundno aff
I. S. Elkamash, I. Kourakis

Bibliographic record

VenuePhysics of Plasmas · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsnot available
FundersQueen's UniversityUniversidade de São PauloFundação de Amparo à Pesquisa do Estado de São PauloQueen's University Belfast
KeywordsPhysicsCollisionalityPlasmaDissipative systemShock waveIonAtomic physicsDusty plasmaElectronShock (circulatory)Electric fieldShock waves in astrophysicsClassical mechanicsMechanicsQuantum mechanicsTokamak

Abstract

fetched live from OpenAlex

A comprehensive analytical model is introduced for shock excitations in dusty bi-ion plasma mixtures, taking into account collisionality and kinematic (fluid) viscosity. A multicomponent plasma configuration is considered, consisting of positive ions, negative ions, electrons, and a massive charged component in the background (dust). The ionic dynamical scale is focused upon; thus, electrons are assumed to be thermalized, while the dust is stationary. A dissipative hybrid Korteweg–de Vries/Burgers equation is derived. An analytical solution is obtained, in the form of a shock structure (a step-shaped function for the electrostatic potential, or an electric field pulse) whose maximum amplitude in the far downstream region decays in time. The effect of relevant plasma configuration parameters, in addition to dissipation, is investigated. Our work extends earlier studies of ion-acoustic type shock waves in pure (two-component) bi-ion plasma mixtures.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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