In Situ Calibration Systems for the Pacific Ocean Neutrino Experiment
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a cubic-kilometer scale neutrino telescope to be deployed in the northern Pacific Ocean off the West Coast of Canada. P-ONE will observe high-energy neutrinos using an array of kilometer tall mooring lines instrumented with P-ONE Optical Modules (P-OMs) which detect Cherenkov light from neutrino-induced secondary particles within the detector volume. To accurately understand the signals from incident neutrinos, the optical properties of seawater, detector geometry, and optical backgrounds must be precisely calibrated. However, the ocean is a dynamic environment where these parameters can vary over time. To achieve this goal, P-ONE includes a variety of calibration systems for both localized and ranged real time detector calibration measurements. These include integrated small, fast light flashers for optical inter-module measurements, acoustic receivers for spatial trilateration, and auxiliary sensors for tilt and orientation measurements. The acoustic positioning system is further complemented with autonomous and cabled acoustic pingers on the seafloor. In addition, some P-OMs in the detector are designed as hybrid calibration modules (P-CALs) which additionally contain long-range, isotropic nanosecond light flashers and cameras. This talk highlights the simulation, development, and field testing of all P-ONE calibration systems
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".