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Record W4312334928 · doi:10.1364/ofs.2022.w4.38

FOS-based thermo-hygrometers in the ATLAS Inner Detector

2022· article· en· W4312334928 on OpenAlexaff
Lorenzo Scherino, M. Schioppa, Alla V. Arapova, Gaia Maria Berruti, Wojtek J. Bock, Anna Borriello, Stefania Campopiano, M. Consales, Andrea Cusano, Flavio Esposito, Agostino Iadicicco, Pavle Mikulić, Tiago Neves, P. Petagna, Giuseppe Quero, Anubhav Srivastava, Patrizio Vaiano, Mauro Zarrelli, Aldobenedetto Zotti, Simona Zuppolini

Bibliographic record

Venue27th International Conference on Optical Fiber Sensors · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsAtlas (anatomy)Large Hadron ColliderDetectorATLAS experimentHygrometerAtlas detectorData acquisitionHumidityRemote sensingEnvironmental scienceComputer scienceEngineeringPhysicsElectrical engineeringNuclear physicsMeteorologyGeologyOperating system

Abstract

fetched live from OpenAlex

We present the main steps of FOS (based on LPG and FBG) installation in the CERN-ATLAS experiment for temperature and humidity measurements, from laboratory calibrations and data acquisition chain development, to their installation and operation.

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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.037
GPT teacher head0.278
Teacher spread0.241 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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