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Record W2054735098 · doi:10.1063/1.2957777

Filterscopes: Spectral line monitors for long-pulse plasma devices

2008· article· en· W2054735098 on OpenAlexaff
N.H. Brooks, R. J. Colchin, D. T. Fehling, D. L. Hillis, Y. Mu, E.A. Unterberg

Bibliographic record

VenueReview of Scientific Instruments · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Energy
KeywordsKSTARDynodeInstrumentation (computer programming)PhotomultiplierPlasmaData acquisitionPulse durationPhysicsMaterials scienceComputer scienceTokamakOpticsLaserNuclear physics

Abstract

fetched live from OpenAlex

A photomultiplier (PMT)-based diagnostic system for monitoring spectral lines along multiple viewchords, named the "Filterscope" [R. J. Colchin et al., Rev. Sci. Instrum. 74, 2068 (2003)], is currently in use at the DIII-D, NSTX, and CDX-U fusion plasma devices in the US, and has been installed at the KSTAR device in Korea. This diagnostic has recently been upgraded for application to long-pulse devices, such as KSTAR, EAST in China, and the future ITER in France. A new data acquisition system, employing the PXI instrumentation platform with an embedded Windows microprocessor controller, can simultaneously record up to 72 channels at 100 kHz sampling rates for plasma periods lasting up to 20 min. Based on the average signal level during an adjustable time interval (100 ms in the present DIII-D implementation), the controller digitally adjusts PMT dynode voltage throughout the course of a discharge, thereby maintaining the output signals at a level where they are neither saturated nor dominated by digitizer noise. The new system's ability to accommodate large variations in source strength, discharge to discharge and within a single discharge, has proved particularly valuable during DIII-D operations, since changes between top, bottom, and double-null divertor magnetic configurations lead to large temporal variations in signal brightness.

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)0.000
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 score0.941
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0430.000

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 head0.031
GPT teacher head0.304
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 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

Citations16
Published2008
Admission routes1
Has abstractyes

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