Acoustic versus visual monitoring of Cetaceans in coastal habitats
Bibliographic record
Abstract
Monitoring the aquatic soundscape provides critical information about marine mammal habitat use. It is essential for developing mitigation strategies for areas with expanding marine shipping and industrial activity. 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 in real-time. The acoustic monitoring is supplemented with a land-based visual observatory that oversees the same area of approximately 200 km2. Automated detectors have been developed for vocalizations of humpback whales, orcas, and fin whales. Acoustic data and visual surveys were analyzed for a period of more than 100 days between 2017 and 2018. We present an overview of the acoustic detection functions and their performance by call type and summarize visual survey procedures. Cetacean activity derived from automated acoustic detections and visual observations is analyzed. Finally, acoustic and visual survey methods are compared to assess a) the fraction to which different species are acoustically active while in the area, and b) the effectiveness of both acoustic and visual monitoring efforts for the purpose of cetacean monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".