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Record W2020571000 · doi:10.1063/1.3432120

Ion temperature gradient driven transport in tokamaks with square shaping

2010· article· en· W2020571000 on OpenAlexaff
N. Joiner, W. Dorland

Bibliographic record

VenuePhysics of Plasmas · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhysicsTokamakPlasmaIonSquare (algebra)Atomic physicsHeat fluxInstabilityFlux (metallurgy)Temperature gradientThermalMechanicsNuclear physicsThermodynamicsHeat transferQuantum mechanicsMaterials science

Abstract

fetched live from OpenAlex

Advanced tokamak schemes which may offer significant improvement to plasma confinement on the usual large aspect ratio Dee-shaped flux surface configuration are of great interest to the fusion community. One possibility is to introduce square shaping to the flux surfaces. The gyrokinetic code GS2 [Kotschenreuther et al., Comput. Phys. Commun. 88, 128 (1996)] is used to study linear stability and the resulting nonlinear thermal transport of the ion temperature gradient driven (ITG) mode in tokamak equilibria with square shaping. The maximum linear growth rate of ITG modes is increased by negative squareness (diamond shaping) and reduced by positive values (square shaping). The dependence of thermal transport produced by saturated ITG instabilities on squareness is not as clear. The overall trend follows that of the linear instability, heat and particle fluxes increase with negative squareness and decrease with positive squareness. This is contradictory to recent experimental results [Holcomb et al., Phys. Plasmas 16, 056116 (2009)] which show a reduction in transport with negative squareness. This may be reconciled as a reduction in transport (consistent with the experiment) is observed at small negative values of the squareness parameter.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.238
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

Citations3
Published2010
Admission routes1
Has abstractyes

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