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Calibration Hardware for SNO+

2020· article· en· W2998990405 on OpenAlexaff
R. Bayes

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsScintillatorCalibrationNeutrinoContext (archaeology)DetectorPhotomultiplierPhysicsSolar neutrinoNeutrino detectorNuclear physicsNeutrino oscillationNuclear engineeringOpticsEngineering

Abstract

fetched live from OpenAlex

Abstract The SNO+ experiment has a varied neutrino physics program that includes a neutrino-less double beta decay experiment in addition to reactor, solar, and geoneutrino measurements. SNO+ uses the architecture of SNO, using an acrylic vessel filled with scintillator as its neutrino target suspended in a water volume. At this time data is being collected with the acrylic vessel filled with water in preparation for the scintillator phase. An essential component to the successful execution of this physics program is a calibration of the optical and energetic response of the detector. Calibrations are underway using a laser-driven light source and radioactive gas sources, such as Nitrogen-16, that are to be lowered into the detector vessel on an umbilical. The position can be manipulated in 1-D using the umbilical retrieval mechanisms (URM) or in 2-D using ropes to guide the source off-axis. The sources and drive systems will be presented here with the goals of the calibration in the context of its impact on the SNO+ physics program.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0520.016

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 head0.064
GPT teacher head0.305
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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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