Bibliographic record
Abstract
Hyper-Kamiokande or Hyper-K is the next generation Water Cherenkov detector being constructed in Kamioka, Japan, following the successful running Super-Kamiokande experiment. The Hyper-Kamiokande will not only measure neutrinos from the accelerator at J-PARC, Tokai in Japan, as a long-baseline neutrino experiment but also from the natural sources like the sun and the atmosphere. The design and construction of the detector is progressing currently, and the experiment is expected to start taking data in 2027. Apart from advanced PMT configurations, the Hyper-K will also have an Intermediate Water Cherenkov Detector (IWCD) at a distance of approximately a km from the neutrino-source, while the far detector will be located 295 km away. The designs and status of this enormous detector Hyper-K, which is currently under-construction, the status of the near or intermediate detectors, and the main and expected physics capabilities of the experiment are described in this report.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".