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Record W2052033983 · doi:10.1063/1.3600455

Diamagnetic loop measurement in Korea Superconducting Tokamak Advanced Research machine

2011· article· en· W2052033983 on OpenAlexfundno aff
J.G. Bak, S. G. Lee, Han-Seek Kim

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory Health
KeywordsKSTARDiamagnetismTokamakPlasmaStellaratorElectron cyclotron resonanceCyclotronPhysicsAtomic physicsLimiterMaterials scienceNuclear magnetic resonanceNuclear physicsMagnetic fieldElectrical engineering

Abstract

fetched live from OpenAlex

Diamagnetic loop (DL), which consists of two poloidal loops inside the vacuum vessel, is used to measure the diamagnetic flux during a plasma discharge in the Korea Superconducting Tokamak Advanced Research (KSTAR) machine. The vacuum fluxes in the DL signal can be compensated up to 0.1 mWb by using the coefficients, which are obtained from experimental investigations, in the vacuum flux measurements during vacuum shots under same operational conditions of magnetic coils for plasma experiment in the KSTAR machine. The maximum error in the diamagnetic flux measurement due to the errors of the coefficients was estimated as ∼0.22 mWb. From the diamagnetic flux measurements for the ohmically heated circular plasmas in the KSTAR machine, the stored energy agrees well with the estimated kinetic energy within the discrepancy of 25%. When the electron cyclotron heating, the neutral beam injection, and the ion cyclotron resonance heating are added to the ohmically heated limiter plasmas, the additional heating effects can be clearly observed from the increase of the stored energy evaluated in the DL measurement.

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)0.006
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 score0.963
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0950.000

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 head0.119
GPT teacher head0.339
Teacher spread0.220 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

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