Integrating passive acoustic and visual surveys for marine mammals in coastal habitats
Bibliographic record
Abstract
Dual-platform studies in confined marine habitats can contribute to the calibration of passive acoustic data for purposes of density estimation. We established a long baseline hydrophone array in northern coastal British Columbia, Canada, that consists of four synchronized, bottom-mounted, continuously recording hydrophones. Automated detectors have been developed for vocalizations of humpback whales, orcas, and fin whales. Visual surveys are conducted from an observation platform overseeing the same area of approximately 200 sq. km. Here we compare humpback whale detections between the visual and acoustic platforms for 78 days of surveys in 2018. Two call types were analyzed: bubble net feeding calls and miscellaneous other. Acoustic surveys yielded higher detection rates (15x) of bubble net feeding groups than visual surveys, but visual detection rates of humpback whales engaged in other behaviors were twice that of acoustic surveys. When summarizing data into 24-hour periods, we found strong correlations between visual and acoustic detection rates for both call types. These correlations were strongest when all visual detections were included, and weakest when we excluded distant sightings from our dataset. These preliminary results are an encouraging first step in the derivation of call rates and the estimation of local species densities using passive acoustics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".