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Record W2737971962 · doi:10.1017/s0022377817000551

Predictive simulations of operation scenarios for EAST with METIS code

2017· article· en· W2737971962 on OpenAlexaboutno aff
Y. C. Li, Miaohui Li, B. Ding, J.F. Artaud, Y. Peysson, A. Ekedahl, Mao Wang, Xingjun Wang, Handong Xu, Jiafang Shan, F. K. Liu

Bibliographic record

VenueJournal of Plasma Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsTokamakPhysicsCode (set theory)Current (fluid)Nuclear engineeringAerospace engineeringSimulationComputer sciencePlasmaNuclear physicsEngineering

Abstract

fetched live from OpenAlex

Upgraded heating and current drive (H/CD) systems have been equipped on the Experimental Advanced Superconducting Tokamak (EAST). With the upgraded H/CD systems, the operation space of EAST is extended, and the ability to achieve higher performance is improved. In this paper, a 0.5 dimension transport code named Minute Embedded Tokamak Integrated Simulator (METIS) is applied to predict the EAST operation space and to assess the current drive capability of the 4.6 GHz lower hybrid current drive system. Predictive simulation of several EAST scenarios, including steady-state high confinement mode (H-mode), advanced regime, high normalized beta and high electron temperature, are also performed with the available H/CD systems. The simulation results provide a guidance for forthcoming advanced EAST experiments.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.301
Teacher spread0.273 · 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
Published2017
Admission routes1
Has abstractyes

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