The feasibility of a multipurpose acoustic network in Baffin Bay
Bibliographic record
Abstract
Operating autonomous underwater vehicles at high latitudes is a challenge because ice cover prevents the use of GPS or data communications. As a result, our scientific observations are biased towards late spring, summer, and early autumn when ships can navigate and autonomous platforms can safely surface. To address this problem, we studied the feasibility of a basin-scale multipurpose acoustic network called the “Baffin Bay Acoustic Navigation and Communication System” (BBANC). BBANC would deploy broadband low frequency sources and receivers, offering one-way communication, acoustic positioning, and acoustic thermometry services. Passive acoustic listening elements would support the study of marine mammal communication and ambient noise from ships, ocean-based resource exploitation, and ice dynamics, as well as gate acoustic source operation in the presence of marine mammals. We describe the challenges and design parameters for such a system, as well as define additional acoustic and remote sensing measurements required to complete a system design. Drawing from a large database of Baffin Bay hydrography, we present simulations of under-ice sound speed conditions, ice properties derived from satellite remote sensing and upward looking sonar data, and modelled acoustic propagation paths in an ice-covered Baffin Bay. We also assess the feasibility of non-coherent and coherent communication.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".