Optical analysis of the Pacific Ocean Neutrino Experiment (P-ONE) site using data from the first pathfinder mooring
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is an initiative by a collaboration of Canadian and German universities as well as Ocean Networks Canada (ONC) to develop a new large-scale neutrino telescope 2600 m below the ocean off the coast of western Canada. While the instrumented volume needs to be at least on the order of km³ for the physics goals of P-ONE to be met, the density of photo sensors needs to be kept as low as possible in order to minimize construction costs. Naturally, this puts very high demands on the optical properties of water at the deployment site. Ideally, the water should exhibit minimal photon extinction and scattering to optimize the light yield and timing needed for reconstructing neutrino-induced Cherenkov light flashes. In addition, a low light background from natural undersea sources such as bioluminescence and K40 radioactive decay is necessary for achieving high sensitivity to neutrino events. In order to evaluate the proposed site for P-ONE, two pathfinder missions have been deployed successfully, one in 2018 and the other in 2020. We present the results from the first mission that was primarily aimed at evaluating the optical properties of the site in terms of attenuation length, and backgrounds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".