MétaCan
Menu
Back to cohort
Record W2075322321 · doi:10.1063/1.3593110

Determination of radial location of rotating magnetic islands by use of poloidal soft x-ray detector arrays in the STOR-M tokamak

2011· article· en· W2075322321 on OpenAlexafffund
M. Dreval, C. Xiao, S. Elgriw, D. Trembach, S. Wolfe, Akira Hirose

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsPlasmionique (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTokamakPhysicsDetectorRADIUSPlasmaPlasma diagnosticsMagnetic fieldAtomic physicsLine (geometry)OpticsLine-of-sightComputational physicsAstrophysicsNuclear physicsGeometry

Abstract

fetched live from OpenAlex

A technique is presented for determining the radial location of the rotating magnetic islands in the STOR-M tokamak by use of soft x-ray (SXR) detector arrays. The location is determined by examining the difference in the integrated SXR emission intensities through two adjacent lines of sight. A model for calculating dependence of the line integrated SXR emission intensity on the radius, the mode numbers and the magnetic island geometry, has been developed. The SXR difference signal shows phase inversion when the impact parameter of the line of sight sweeps across the magnetic islands. Experimentally, the difference SXR signals significantly reduce noise and suppress the influence of background plasma fluctuations through common mode rejection when a dominant mode exists in the STOR-M tokamak. The radial locations of the m = 2 magnetic islands have been determined under several experimental conditions in the STOR-M discharges. With the decrease in the tokamak discharge current and thus the increase of the safety factor at the edge, the radial location of the m = 2 magnetic islands has been found to move radially inward.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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 designOther design
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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueReview of Scientific InstrumentsSame topicMagnetic confinement fusion researchFrench-language works237,207