A Baffin Bay Acoustic Navigation and Communication System
Bibliographic record
Abstract
In open water, autonomous floats and gliders can periodically surface and use GPS to determine their position. Iridium satellite communication can be used to send data back to land-based ocean data centers for analysis, research and operational decision-making. However, autonomous operation in the Arctic is not currently possible because ice cover prevents floats and gliders from positioning (geolocating) their measurements as well as relaying data to shore for long periods; neither GPS nor satellite data communications are possible under the water. As a result, our current set of scientific observations of the Canadian Arctic is biased towards spring and summer when it is navigable by ships and when autonomous platforms can safely surface. To truly comprehend winter Arctic waters in the manner that has been achieved in world’s more temperate oceans, floats and gliders require additional capability for underwater geolocation and periodic communication. We propose a Baffin Bay Acoustic Navigation and Communication System (BBANC), which will use broadband low-frequency sources to provide basin-wide coverage of RAFOS-style signals for acoustic positioning of underwater assets. Low-frequency acoustic receivers would be collocated with the sources to enable ocean acoustic tomography for long-term study of heat content and currents. Finally, passive acoustic listening systems would enable the study of marine mammal communication and ambient noise from ships, sonar, ocean-based resource exploitation and ice dynamics, as well as gating acoustic source operation in the presence of marine mammals. The BBANC Project currently consists of a collaborative feasibility study to describe the basic challenges and design parameters of such a system, as well as define additional acoustic measurements required to complete a system design. We will present simulation of under-ice sound speed from historical T-S profiles, ice properties derived from satellite remote sensing, and modelled acoustic propagation in an ice-covered Baffin Bay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".