Counting odontocetes from click train detections using multiple independent autonomous acoustic sensors
Bibliographic record
Abstract
Passive acoustic monitoring (PAM) is often suggested as an effective technology to mitigate impacts from anthropogenic activities; however, the ability to reliably and efficiently detect, locate, and count cetaceans using PAM is still in development. One particularly useful application of PAM is species density estimation, which requires an estimate of the number of individuals involved in a detection event. Efforts have been undertaken to develop methods to directly count the number of vocalizing animals during acoustic detection events. For odontocetes, discrete clicks are almost indistinguishable between individuals, making it more difficult to determine the number of vocalizing animals as the number increases. Using recordings from multiple closely spaced (≈200 m) GuardBuoy sensors deployed on the Canadian Scotian Shelf, cross-sensor correlograms were produced to estimate the number of individual sperm whales, and, as a more challenging case, the number of vocalizing delphinids. Using feature-based multipath reflection discrimination, the raw time series were reduced to a synthetic time series of binary click detections with the multipath arrivals removed. The synthesized click-detection time series were used for the cross-sensor correlograms to generate improved estimates of the number of vocalizing animals as compared with using the raw time series.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".