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Record W4313223409 · doi:10.22323/1.421.0033

The SNO+ Experiment: Reactor & Solar $\nu$ Prospects

2022· article· en· W4313223409 on OpenAlexafffundabout
B. Tam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsQueen's University
FundersCompute CanadaScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaQueen's UniversityCanadian Institute for Advanced ResearchFundação para a Ciência e a TecnologiaU.S. Department of EnergyNational Science Foundation
KeywordsScintillatorPhysicsSolar neutrinoNeutrinoNuclear physicsNeutrino detectorCherenkov radiationDetectorCherenkov detectorDouble beta decayObservatoryParticle physicsNeutrino oscillationOpticsAstrophysics

Abstract

fetched live from OpenAlex

The SNO+ experiment is a large-scale, multipurpose neutrino experiment situated 2 km underground at SNOLAB in Canada. Successor to the Sudbury Neutrino Observatory, the SNO+ detector has inherited much of the original infrastructure including the 12-m diameter acrylic vessel which serves as the main detector body. Initially filled with ultrapure water, the SNO+ experiment has completed operations as a water Cherenkov detector, having set new limits on multiple invisible nucleon decay modes, performed measurements on $^8\mathrm{B}$ solar neutrinos, and made the first observation of reactor antineutrinos in pure water. The detector medium has now been replaced with liquid scintillator, and a new physics programme is being pursued including measurements of solar neutrinos and $\Delta$m$^2_{12}$ from reactor antineutrinos. The liquid scintillator will be doped with >4 tonnes of $\mathrm{^{nat}Te}$ to enable a search for neutrinoless double beta decay.

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)0.005
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0510.022

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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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 designNot applicable
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".

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Citations1
Published2022
Admission routes3
Has abstractyes

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