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Record W1972187131 · doi:10.1080/10420150.2011.615315

Numerical simulations of the soft X-ray signals for determination of radial locations of the magnetic islands in the STOR-M tokamak

2011· article· en· W1972187131 on OpenAlexafffund
M. Dreval, C. Xiao, Akira Hirose

Bibliographic record

VenueRadiation effects and defects in solids · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEmissivityPhysicsAmplitudeMagnetohydrodynamicsTokamakMagnetohydrodynamic driveComputational physicsMagnetic fieldAstrophysicsOpticsPlasmaNuclear physics

Abstract

fetched live from OpenAlex

A novel soft X-ray (SXR) data analysis technique has been recently developed and applied to the SXR data from the STOR-M tokamak to determine the radial locations of rotating magnetohydrodynamic (MHD) modes by examining the difference signals between two neighboring SXR channels [Rev. Sci. Instrum. 2011, 82, 053503]. In the simulations presented in this paper, the realistic SXR viewing angles and different SXR emissivity models are included. Comparison is made for two emissivity models: (a) a model with a constant SXR emissivity within the island and zero background equilibrium emissivity outside the island and (b) Gaussian emissivity perturbation inside the island with the parabolic background equilibrium emissivity distribution. Simulations with both emissivity profiles reveal the same criteria for determination of the radial position of the magnetic islands: (a) the phase reversal of the oscillations in the difference signals and (b) increase in the oscillation amplitude near the island. The results demonstrated the robustness of the techniques suitable for various kinds of emissivity perturbations due to rotating magnetic islands.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.435

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)0.0000.000

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 head0.263
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2011
Admission routes2
Has abstractyes

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