The Acoustic Calibration System for the Pacific Ocean Neutrino Experiment
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a proposed neutrino telescope that will explore the deepest reaches of the universe through cosmic neutrinos. Located 2600 meters below sea- level in the Cascadia Basin off the coast of Vancouver, Canada, the detector will utilise Cherenkov radiation from secondary particles emitted by high energy neutrino interactions as a means of detection. These emissions are digitized by optical modules lining the vertically deployed strings. Accordingly, the accuracy of detection will directly correlate with the understanding of the medium, and the locations of the optical modules. The ocean currents present a hurdle in this respect. A common tracking method is utilising acoustic detectors and emitters in a methodology known as trilateration. The acoustic modules will use piezoelectric disks to detect the vibrations produced by acoustic beacons for this calibration process. P-ONE will be designed with these additional acoustic detectors for tracking, and will be tested with Ocean Networks Canada’s Marine Test Facility. In this contribution we will cover the simulated performance of the acoustic trilateration and the status of the P-ONE Acoustic Calibration System.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".