Bibliographic record
Abstract
SNO+ is a kilotonne-scale, liquid scintillator-based neutrino detector housed in the underground \nfacilities of SNOLAB at Creighton Mine, Sudbury. SNO+ is capable of detecting bursts of neutrinos \nreleased by nearby core-collapse supernovae among other physics goals. For such an event, stress \ntesting is required to ensure that a burst of supernova neutrino events can be reliably read out \nand recorded by the electronics and data acquisition system to avoid data pileup and limit event \nseparation. During a supernova, SNO+ needs to be able to record the burst, send a timely alert \nto the astronomical community, and quickly analyze and interpret the data. \nThe supernova calibration system (SNC+) for SNO+ simulates the light produced by interactions \nof neutrinos from a supernova within the liquid scintillator target using pulsed, visible light \nfrom a laser diode. The SNC+ is a data-driven pulser capable of producing high-powered, ns-scale \npulses with repetition rates up to 12.5 MHz. Each photon pulse is expected to deposit energy \nof up to 70 MeV within the liquid scintillator of the SNO+ detector. The light from the SNC+ \nlaser diode will be delivered isotropically within the SNO+ detector by ber optics and a di using \nglass laserball. The SNC+ has undergone design, parts procurement, construction, assembly, and \ninitial-stage testing for this thesis research.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".