Pacific Ocean Neutrino Experiment: Expected performance of the first cluster of strings
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a proposed large-volume neutrino telescope in the Northeast Pacific Ocean, off the coast of Vancouver Island, Canada. With more than one cubic-kilometer of instrumented deep sea volume, P-ONE will target measuring high-energy neutrinos to shed light on the nature of astrophysical accelerators and the cosmos. With low expected scattering in the deep ocean, water-based detectors theoretically allow for sub-degree event resolution but carry various challenges. With changing ocean currents, and an abundance of organic matter, the detector geometry, water optical properties, and bioluminescent light background vary with time. This dynamic environment of the deep ocean requires rugged detector technologies and multiple, precise calibration and monitoring systems in order to enable and maintain the detector’s full scientific potential. In cooperation with Ocean Networks Canada (ONC), the P-ONE collaboration aims to develop long-lived, deep-sea detector systems which target continuous and precise monitoring to overcome these challenges. The first mooring of P-ONE will be deployed between 2024 and 2025, and will provide first insights into the performance of the developed detector systems. Following this first step, this work summarizes the ongoing efforts of the P-ONE collaboration targeting the development, simulation and operation of the first cluster of strings, and will present the expected performance of the calibration systems and physics potential.
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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 | 0.007 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".