Estimation of surface layer and Pacific summer water properties from acoustic transmissions in the Beaufort duct using a tomographic array during 2016–2017
Bibliographic record
Abstract
The 2016–2017 Canada Basin Acoustic Propagation Experiment (CANAPE) was conducted to assess the effects of the changing Beaufort Gyre on low-frequency underwater acoustic propagation and ambient sound. A 150-km radius ocean acoustic tomography array was deployed with six transceivers and a distributed vertical line array (DVLA) measuring the impulse responses every four hours with broadband signals centered from 172.5 to 275 Hz. The nominal transceiver source depth was 175-m, placing them near the Beaufort duct axis, and the 60 hydrophone DVLA spanned 50 to 600 m. The Beaufort duct (approximately 90-m to 240-m depth) and the surface layer (approximately 0 to 90-m depth) form a coupled double-duct system. Observed arrivals in this system show reverse dispersion with the lowest Beaufort duct modes arriving first and higher double duct modes making up a transmission finale. In this talk, we investigate the oceanographic information content contained in the first and last arrivals which are the easiest to detect and track. The first arrival shows fluctuations from eddies, tides/inertial oscillations, and small seasonal heating/cooling. The last arrival shows a strong seasonal heating/cooling signal but is un-trackable during periods of significant ice cover due to enhanced transmission loss.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".