CNO neutrino Grand Prix: the race to solve the solar metallicity problem
Bibliographic record
Abstract
Several next-generation experiments aim to make the first measurement of the neutrino flux from the Carbon-Nitrogen-Oxygen (CNO) solar fusion cycle. We calculate how much time these experiments will need to run for in order to measure this flux with enough precision to tell us the metal content of the Sun's core, and thereby help to solve the solar metallicity problem. For experiments looking at neutrino-electron scattering, we find that SNO+ will measure this CNO neutrino flux with enough precision after five years in its pure scintillator mode, provided its 210 Bi background is measured to 1% accuracy. By comparison, a 100 ton liquid argon experiment such as Argo will take ten years in Gran Sasso lab, or five years in SNOLAB or Jinping. Borexino could obtain this precision in ten years, but this projection is very sensitive to background assumptions. For experiments looking at neutrino-nucleus scattering, the best prospects are obtained for low-threshold solid state detectors (employing either germanium or silicon). These would require new technologies to lower the experimental threshold close to detection of single electron-hole pairs, and exposures beyond those projected for next-generation dark matter detectors.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".