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Record W3033727951 · doi:10.1063/5.0005868

Optical fiber interferometer application for high electron density measurements on compact torus plasmas

2020· article· en· W3033727951 on OpenAlexaff
Sen Zhang, Chen Chen, Tao Lan, Weixing Ding, G. Zhuang, Wenzhe Mao, Shijie Lan, Jie Wu, Hangqi Xu, Tijian Deng, Junfeng Zhu, Jiaren Wu, Yiming Zu, Defeng Kong, Shoubiao Zhang, Yuan Yao, Zian Wei, Zixi Liu, Haiyang Zhou, Hai Wang, Xiaohui Wen, A. D. Liu, Jinlin Xie, Hong Li, C. Xiao, Wandong Liu

Bibliographic record

VenueReview of Scientific Instruments · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
FundersNational Magnetic Confinement Fusion Program of ChinaNational Natural Science Foundation of China
KeywordsInterferometryOpticsAstronomical interferometerPlasma diagnosticsPlasmaMach–Zehnder interferometerElectron densityPhysicsWavelengthPhase (matter)Materials scienceOptical fiberNoise (video)Fiber

Abstract

fetched live from OpenAlex

An optical fiber Mach–Zehnder interferometer at a wavelength of 1.55 µm has been developed for measurements of high electron density on compact torus (CT) plasmas with a high time resolution of 0.1 µs and high phase resolution of 6.4 × 10−4 rad. To improve density measurement accuracy, the phase noise of the interferometer has been investigated in detail and optimized. In the bench test, the interferometer was calibrated using a piezoelectric ceramic actuator with known stroke. Initial results on CT plasma show that the optical fiber interferometer provides reliable density measurements at two spatial locations and the bulk velocity of plasma can be determined by the method of time of flight.

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 score0.915
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.044
GPT teacher head0.303
Teacher spread0.259 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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