Constraints on neutrino parameters from neutral-current solar neutrino measurements
Bibliographic record
Abstract
We generalize the pull approach to define the ${\ensuremath{\chi}}^{2}$ function to the analysis of the data with correlated statistical errors. We apply this method to the analysis of the Sudbury Neutrino Collaboration data obtained in the salt phase. In the global analysis of all the solar neutrino and KamLAND data we find the best fit (minimum ${\ensuremath{\chi}}^{2})$ values of neutrino parameters to be ${\mathrm{tan}}^{2}{\ensuremath{\theta}}_{12}\ensuremath{\sim}0.42$ and $\ensuremath{\delta}{m}_{12}^{2}\ensuremath{\sim}7.1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}{\mathrm{eV}}^{2}.$ We confirm that the maximal mixing is strongly disfavored while the bounds on $\ensuremath{\delta}{m}_{12}^{2}$ are significantly strengthened.
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 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".