Impact of sound-speed structure on acoustic localization of autonomous underwater vehicles in the Canada Basin
Bibliographic record
Abstract
In recent decades, the Canada Basin’s upper ocean structure has undergone changes with the intrusion of warmer Pacific Ocean waters and continued surface warming. These changes have direct implications for underwater acoustic propagation including the formation of a strong subsurface duct located around 180 m depth, referred to as the Beaufort duct. In summer 2016, a pentagonal array of tomography sources moored within the Beaufort duct over a region with a radius of approximately 150 km was deployed for a year to study acoustic propagation in this environment. In the summer of 2016 and 2017, two autonomous underwater vehicles (AUVs) profiled the upper 750 m of the water column. The AUVs, equipped with hydrophones, collected temperature and salinity profiles along with recordings of signals transmitted from the moored acoustic sources at ranges up to 530 km. In situ measurements are used to generate an empirical sound-speed perturbation field for acoustic predictions, used here to estimate acoustic ranging between moored sources and the AUV by matching received acoustic arrivals to the range-dependent acoustic predictions. Vehicle data are leveraged to Doppler correct ranging and to constrain localization solutions. Localization improvements and solutions within the tomographic array will be presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".