Low energy threshold analysis of the phase I and phase II data sets of the Sudbury neutrino observatory
Bibliographic record
Abstract
Results are reported from a joint analysis of Phase I and Phase II data from the Sudbury Neutrino Observatory. The effective electron kinetic energy threshold used is T{sub eff} = 3.5 MeV, the lowest analysis threshold yet achieved with water Cherenkov detector data. In units of 10{sup 6} cm{sup -2} s{sup =1}, the total flux of active-flavor neutrinos from {sup 8}B decay in the Sun measured using the neutral current (NC) reaction of neutrinos on deuterons, with no constraint on the {sup 8}B neutrino energy spectrum, is found to be {Phi}{sub NC} = 5.140{sub -0.158}{sup +0.160}(stat){sub -0.117}{sup +0.132}(syst). These uncertainties are more than a factor of two smaller than previously published results. Also presented are the spectra of recoil electrons from the charged current reaction of neutrinos on deuterons and the elastic scattering of electrons. A fit to the SNO data in which the free parameters directly describe the total {sup 8}B neutrino flux and the energy-dependent Ve survival probability provides a measure of the total {sup 8}B neutrino flux {Phi}{sub 8{sub B}} = 5.046{sub -0.152}{sup +0.159}(stat){sub -0.123}{sup +0.107}(syst). Combining these new results with results of all other solar experiments and the KamLAND reactor experiment yields best-fit values of the mixing parameters of {theta}{sub 12} = 34.06{sub -0.84}{sup +1.16} degrees and {Delta}m{sub 21}{sup 2} = 7.59{sub -0.21}{sup +0.20} x 10{sup -5} eV{sup 2}. The global value of {Phi}{sub 8{sub B}} is extracted to a precision of {sub -2.95}{sup +2.38}%. In a three-flavor analysis the best fit value of sin{sup 2} {theta}{sub 13} is 2.00{sub -1.63}{sup +2.09} x 10{sup -2}. Interpreting this as a limit implies an upper bound of sin{sup 2} {theta}{sub 13} < 0.057 (95% C. L.).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".