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Record W4214773900 · doi:10.22323/1.380.0274

Status and Prospects of the SNO+ Experiment

2022· article· en· W4214773900 on OpenAlexfundaboutno aff
A. S. Inácio

Bibliographic record

VenueProceedings of Particles and Nuclei International Conference 2021 — PoS(PANIC2021) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaQueen's UniversityCompute CanadaCanadian Institute for Advanced ResearchDeutsche ForschungsgemeinschaftFundação para a Ciência e a TecnologiaU.S. Department of EnergyNational Science Foundation
KeywordsScintillatorDetectorCherenkov radiationNeutrinoPhysicsNuclear physicsCherenkov detectorNeutrino detectorSolar neutrinoCalibrationDouble beta decayParticle physicsPhotomultiplierNeutrino oscillationOptics

Abstract

fetched live from OpenAlex

SNO+ is a large volume liquid scintillator experiment for neutrino physics located at SNOLAB, Canada. Using the 12-m diameter acrylic vessel and the PMT array of the SNO detector, SNO+ has the primary physics goal of searching for the neutrinoless double-beta decay of the $^{130}$Te isotope. Data collected will also be used to explore additional physics topics such as reactor antineutrino oscillations, solar neutrinos, geoneutrinos, and to search for exotic physics. SNO+ completed its first phase as a pure-water Cherenkov detector, which focused on detector commissioning and calibration, and published its first physics results. Currently, the detector is completely filled with liquid scintillator, to which 1.3 tonnes of $^{130}$Te is expected to be loaded in late 2022.

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)0.030
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0070.009
Open science0.0030.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0150.008

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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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