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Record W3112869896 · doi:10.1142/s0217751x20440121

SNO: Recent new results

2020· article· en· W3112869896 on OpenAlexaboutno aff
J. Caravaca Rodríguez

Bibliographic record

VenueInternational Journal of Modern Physics A · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsNeutrinoParticle physicsNeutrino oscillationCOSMIC cancer databaseMuonNuclear physicsCosmic neutrino backgroundCosmic rayMeasurements of neutrino speedSolar neutrino problemNeutronSolar neutrinoAstrophysics

Abstract

fetched live from OpenAlex

The Sudbury Neutrino Observatory (SNO), whose main purpose was to study the neutrinos produced in the Sun, demonstrated that neutrinos can change flavor and, thus, they are massive particles. SNO detected and recorded neutrino and cosmic ray interactions from 1999 to 2006 and several analyses have been completed in the past year using legacy data. We present the results of the most recent ones: the measurements of neutron production in atmospheric neutrino interactions and neutron production by cosmic muons, a search for Lorentz symmetry violation in neutrino oscillations and a search for neutrino decay. A few other analyses are ongoing and we comment about their goal and status.

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)0.018
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.008
Science and technology studies0.0010.002
Scholarly communication0.0060.007
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.008

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 head0.064
GPT teacher head0.338
Teacher spread0.274 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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