Pacific Ocean Neutrino Experiment (P-ONE): prototype line development
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a new initiative to construct one of the world’s largest neutrino detectors in the deep Pacific Ocean off the coast of British Columbia, Canada. Located in the Cascadia Basin region, P-ONE builds on a number of key strengths within the Canadian oceanographic community. The Cascadia Basin monitoring site is part of the NEPTUNE observatory of Ocean Networks Canada (ONC), which provides power and data connections to various deep ocean sites, accessible to experiments. In cooperation with ONC, the collaboration successfully deployed two pathfinder experiments, the STRAW projects, in 2018 and 2020, respectively. These pathfinder mooring lines aim to measure the optical and ambient background characteristics of the Cascadia Basin in a depth of 2660m. The P-ONE prototype line is the successor of these mooring lines and the next step towards the P-ONE neutrino observatory. The main objective of the prototype line lies in the construction, deployment, and operation of a complete P-ONE mooring line as a proof of concept of the individual components. This line will comprise of P-ONE digital optical modules to measure the emerging Cherenkov radiation by neutrino-induced processes and P-ONE calibration devices to provide in-situ calibration of the detector. The prototype line will be complemented by external geometry calibration units to verify the envisioned calibration principles.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".