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Optical Coatings as Mirrors for Optical Diagnostics

2016· article· en· W2232423117 on OpenAlexvenueno aff
L. Marot, G. Arnoux, A. Huber, V. Huber, Ph. Mertens, G. Sergienko, Ernst Meyer

Bibliographic record

VenueJournal of Coating Science and Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
FundersBundesamt für EnergieSwiss Nanoscience InstituteEuratom Research and Training ProgrammeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Commission
KeywordsOpticsOptical coatingMaterials scienceOptoelectronicsComputer scienceNanotechnologyPhysicsCoating

Abstract

fetched live from OpenAlex

The aim of this work was to provide a comprehensive insight concerning coated films which might be used for first mirrors in ITER. The influence of the mirror crystallite size has been addressed as well as the coating techniques to provide nanocrystalline films. Tests of coated mirrors both in laboratories and in tokamaks are reviewed. For the tokamak tests a wide angle camera system has been installed in JET-ILW which is composed of a mirror box with 3 stainless steel mirrors coated with rhodium viewing the torus through a conically shaped aperture. The system delivered the required image quality for plasma monitoring and wall protection. No or insignificant degradation of the optical transmittance has been observed during the experimental campaign in 2014 with about 3000 plasma pulses in different magnetic field configurations.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.314
Teacher spread0.295 · 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 designBench or experimental
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

Citations19
Published2016
Admission routes1
Has abstractyes

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