Data Quality and Run Selection for the SNO+ experiment
Bibliographic record
Abstract
Abstract The SNO+ detector main physics goal is the search for neutrinoless double-beta decay, a rare process which if detected, will prove the Majorana nature of the neutrinos and provide information on the absolute scale of the neutrino absolute mass. Additional physics goals of SNO+ include the study of solar neutrinos, anti-neutrinos from nuclear reactors and the Earth’s natural radioactivity as well as Supernovae neutrinos. Located in the SNOLAB underground physics laboratory (Canada), it will re-use the SNO experiment infrastructure with the 12 m diameter spherical volume filled with 780 tons of Te-loaded liquid scintillator. A short phase with the detector completely filled with water has started at the end of 2016. It will be followed by a scintillator phase expected to start at the end of this year. Continual careful monitoring of the detector state such as its hardware configuration, slow control information, data handling and triggers is required to ensure the quality of the data taken. Several automatic checks have been put in place for that purpose. This information serves as input to higher level run selection tools that will ultimately perform a final decision on the goodness of a run for a given physics analysis.
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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".