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Record W2076466524 · doi:10.1103/physrevd.62.093004

SNO: Predictions for ten measurable quantities

2000· article· en· W2076466524 on OpenAlexaboutno aff
John N. Bahcall, P. I. Krastev, Alexei Yu. Smirnov

Bibliographic record

VenuePhysical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsCharged currentOscillation (cell signaling)NeutrinoNeutrino oscillationCurrent (fluid)RecoilNuclear physicsRange (aeronautics)ElectronScatteringNeutral currentParticle physicsChemistryQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

We calculate the range of predicted values for $10$ quantities that will be measured by the Sudbury Neutrino Observatory (SNO). We use neutrino oscillation solutions (vacuum and MSW; active and sterile neutrinos) that are globally consistent with all available neutrino data and estimate realistic theoretical and experimental uncertainties. The neutral current to charged current double ratio is predicted to be more than $9\ensuremath{\sigma}$ from the no-oscillation solution for all of the currently favored neutrino oscillation solutions. The best-fit oscillation solutions predict a CC day-night rate difference between $\ensuremath{-}0.1%$ and $+12.5%$ and a NC day-night difference $<0.01%.$ We present also the predicted range for the first and the second moments of the charged current electron recoil energy spectrum, the charged current, the neutral current, and the $\ensuremath{\nu}\ensuremath{-}e$ scattering rates, the seasonal dependence of the charged current rate, and the double ratio of neutrino-electron scattering rate to charged current rate.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.367
Teacher spread0.334 · 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 designSimulation or modeling
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

Citations43
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePhysical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fieldsSame topicNeutrino Physics ResearchFrench-language works237,207