Acoustic receptions at close ranges measured on autonomous underwater vehicles in the Beaufort Sea
Bibliographic record
Abstract
Rapid warming of the Pacific Summer Water layer strengthens a subsurface duct in the Beaufort Sea, allowing for long-range propagation at low frequencies. An array of tomography sources was deployed within the duct as part of the Canada Basin Acoustic Propagation Experiment (CANAPE) to study acoustic propagation in this environment. The moored transceivers provide measurements of acoustic propagation at several ranges from 176 to 285 km. Additionally, two Seaglider vehicles equipped with hydrophone receivers navigated in and around the CANAPE array and recorded the transmissions from the moored sources at ranges as far as 530 km and as close as 2 km. A spatially variable sound speed environment was generated from in-situ data measured by the Seagliders and CTD casts from research vessels. Acoustic arrivals measured on the vehicles were matched to range-dependent acoustic predictions made with a broadband Parabolic Equation model to estimate source-receiver range. Acoustic receptions from multiple moored sources were used to localize the Seagliders. Here, we examine the close range (2–25 km) receptions and their impacts on acoustic localization.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".