Localizing bioacoustic signals with long-baseline hydrophone arrays
Bibliographic record
Abstract
Real-time localization and tracking of vocalizing marine mammals is a powerful tool to a) mitigate the risk of ship strikes and disturbance of the animals as well as b) to understand the impact of anthropogenic noise on habitat use of cetaceans. A long baseline hydrophone array has been installed in Squally Channel, a culturally, ecologically, and economically important marine environment in northern British Columbia, Canada. The array consists of four synchronized bottom-mounted hydrophones that permanently record and radio-transmit data to a land-based laboratory. Automated analysis tools have been developed to detect and localize transient bioacoustic signals from three or more hydrophones in real-time. They comprise the correlation of hydrophone signals, the construction of a replica surface using acoustic propagation models, and signal localization from a spatial likelihood surface. An overview of the localization method is presented. We assess spatial localization precision and discuss random and systematic sources of error stemming from the signal type and quality, bathymetry, sound speed profile, as well as the spatial distribution of hydrophones and acoustic sources. Simulation results are compared to field measurements. Based on our findings, challenges and opportunities associated with localization using long baseline arrays in coastal environments are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".