First gadolinium loading to Super-Kamiokande
Bibliographic record
Abstract
In order to improve Super-Kamiokande’s neutron detection efficiency and to thereby increase its sensitivity to the diffuse supernova neutrino background flux, 13 tons of (gadolinium sulfate octahydrate) was dissolved into the detector’s otherwise ultrapure water from July 14 to August 17, 2020, marking the start of the SK-Gd phase of operations. During the loading, water was continuously recirculated at a rate of 60 m/h, extracting water from the top of the detector and mixing it with concentrated solution to create a 0.02% solution of the Gd compound before injecting it into the bottom of the detector. A clear boundary between the Gd-loaded and pure water was maintained through the loading, enabling monitoring of the loading itself and the spatial uniformity of the Gd concentration over the 35 days it took to reach the top of the detector. During the subsequent commissioning the recirculation rate was increased to 120 m/h, resulting in a constant and uniform distribution of Gd throughout the detector and water transparency equivalent to that of previous pure-water operation periods. Using an Am–Be neutron calibration source the mean neutron capture time was measured to be , which corresponds to a Gd concentration of ppm, as expected for this level of Gd loading. This paper describes changes made to the water circulation system for this detector upgrade, the Gd loading procedure, detector commissioning, and the first neutron calibration measurements in SK-Gd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".