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Development and characterisation of a gas system and its associated slow-control system for an ATLAS small-strip thin gap chamber testing facility

2017· article· en· W2588883821 on OpenAlexaffabout

Bibliographic record

VenueJournal of Instrumentation · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsMcGill University
Fundersnot available
KeywordsCondenser (optics)Atlas (anatomy)Thermoelectric effectThermoelectric coolingQuality assuranceThermoelectric generatorSpectrometerDetector

Abstract

fetched live from OpenAlex

A quality assurance and performance qualification laboratory was built at McGill University for the Canadian-made small-strip Thin Gap Chamber (sTGC) muon detectors produced for the 2019–2020 ATLAS experiment muon spectrometer upgrade. The facility uses cosmic rays as a muon source to ionise the quenching gas mixture of pentane and CO(2) flowing through the sTGC detector. A gas system was developed and characterised for this purpose, with a simple and efficient gas condenser design utilizing a Peltier thermoelectric cooler (TEC). The gas system was tested to provide the desired 45 vol% pentane concentration. For continuous operations, a state-machine system was implemented with alerting and remote monitoring features to run all cosmic-ray data-acquisition associated slow-control systems, such as high/low voltage, gas system and environmental monitoring, in a safe and continuous mode, even in the absence of an operator.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.347

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.266
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2017
Admission routes2
Has abstractyes

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