A Bioinspired Approach to Sound Localization in the Underwater Coastal Environment
Bibliographic record
Abstract
In the shallow water coastal area, visibility is comparatively restricted, yet marine mammals are still able to navigate, manoeuvre, and find food without any noticeable difficulties. Our work has focused particularly on grey whales (Eschrichtius robustus) due to their close association with the shallow water environment. One of their primary food sources is found in kelp beds, in a highly cluttered and acoustically active environment. Because the usefulness of vision is limited by the turbidity of the coastal submarine environment, it is logical to believe they rely heavily on their hearing. Unlike dolphins and porpoises, grey whales do not appear to use active echolocation techniques. We propose therefore that they are making use of the ambient noise for passive acoustic characterisation of their environment. We are investigating what sounds are available to these animals in their feeding grounds and what types of visualisation techniques they might be employing. During the summer of 2006, ambient noise recordings were collected in two bays along the central coast of British Columbia, Canada, where grey whales are known to feed. The array used was a fixed 2-hydrophone design, horizontally separated by a small distance, analogous to a set of ears, and was deployed from a kayak. The acoustic signals were recorded near the surface at a broadband frequency range of 20 Hz to 20 kHz where the water column depths ranged from 3 m to 30 m approximately, and in several distinct environments (deeper water, shallow water with kelp bed, shallow water with bare seabed, surf zone). This paper examines the data collected and develops hypotheses based on Synthetic Aperture and Acoustic Daylight Imaging techniques as possible mechanisms available to these whales to interpret the nearshore sound field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".