Automated Monitoring and Analysis of Marine Mammal Vocalizations in Coastal Habitats
Bibliographic record
Abstract
A partnership of the Gitga'at First Nation, WWF-Canada and the North Coast Cetacean Society has installed a long baseline hydrophone array in Squally Channel; a culturally, ecologically, and economically important marine environment in northern British Columbia (BC), Canada. The array consists of four synchronized bottom-mounted hydrophones that permanently record and radio-transmit data to a land-based laboratory in real-time. The array covers an area of approximately 200 km2to realize long-term and wide-range monitoring of marine mammals. To allow for efficient data analysis, automated detectors for cetacean vocalizations have been developed in collaboration with the University of Victoria. The detection performance has been tested using manually annotated data. We present an overview of the detectors for orca and humpback whales, and their computational and analytic performance as a function of signal-to-noise ratio. The ability to adapt to different ocean environments and target species is demonstrated by applying the detectors to 100 days of archival acoustic data recorded at two different places along the BC coast. Data recorded during pilot studies in 2017 from Squally Channel (approximately 30 days) is used to provide an overview of cetacean vocalization detections. Finally, we show acoustic activity variations and trends based on environmental factors.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".