Bibliographic record
Abstract
The Sudbury Neutrino Observatory (SNO) is a large-volume heavy water Cerenkov detector designed to resolve the solar neutrino problem. SNO observes charged-current interactions with electron neutrinos, neutral-current interactions with all active neutrinos, and elastic-scattering interactions primarily with electron neutrinos with some sensitivity to other flavors. This dissertation presents an analysis of the solar neutrino flux observed in SNO in the second phase of operation, while ~2 tonnes of salt (NaCl) were dissolved in the heavy water. The dataset here represents 391 live days of data. Only the events above a visible energy threshold of 5.5 MeV and inside a fiducial volume within 550 cm of the center of the detector are studied. The neutrino flux observed via the charged-current interaction is [1.71 ± 0.065(stat.)±$0.065\atop{0.068}$(sys.)±0.02(theor.)] x 106cm-2s-1, via the elastic-scattering interaction is [2.21±0.22(stat.)±$0.12\atop{0.11}$(sys.)±0.01(theor.)] x 106cm-2s-1, and via the neutral-current interaction is [5.05±0.23(stat.)±$0.31\atop{0.37}$(sys.)±0.06(theor.)] x 106cm-2s-1. The electron-only flux seen via the charged-current interaction is more than 7σ below the total active flux seen via the neutral-current interaction, providing strong evidence that neutrinos are undergoing flavor transformation as they travel from the core of the Sun to the Earth. The most likely origin of the flavor transformation is matter-induced flavor oscillation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".