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Record W2071872422 · doi:10.1063/1.874091

Charge separation at a plasma edge in the presence of a density gradient

2000· article· en· W2071872422 on OpenAlexafffund
M. Shoucri, E. Pohn, G. Knorr, P. Bertrand, G. Kamelander, Giovanni Manfredi, A. Ghizzo

Bibliographic record

VenuePhysics of Plasmas · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsStem Cell Network
FundersHydro-QuébecInstitut national de la recherche scientifique
KeywordsPhysicsElectric fieldIonPlasmaAtomic physicsElectric potentialGyroradiusElectronSpace chargeAdiabatic processKinetic energyDensity gradientVoltageClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

A fully kinetic code for ions [one dimensional (1-D) in space, and using the three velocity dimensions in velocity space] is used to study the problem of the formation of a charge separation with the self-consistent electric field in a plasma in the presence of a density gradient. Electrons are treated using an adiabatic law. Graphical results are presented which follow the formation of a 1-D steady state showing the formation of an oscillating positive potential bump toward the edge of the plasma. These oscillations are closely associated with the gyration of the ions. It is also shown that the presence of a small fraction of impurity ions at the plasma edge can have a significant effect on the rapid buildup of the potential at the edge, and in increasing the charge separation and the associated electric field at the edge, in comparison to the case when no impurity ions are included. The present results show the importance of a kinetic solution to the problem of the equilibrium electric field and charge separation in the presence of a density gradient, and point to the important role played by the finite ions’ gyroradius and the important contribution of impurity ions in this case.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.018
GPT teacher head0.271
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 teacher head, not a consensus.

Study designBench or experimental
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

Citations13
Published2000
Admission routes2
Has abstractyes

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