Deriving an acoustic-based abundance estimate for porpoise species in Pacific Canadian waters
Bibliographic record
Abstract
The Pacific Region International Survey of Marine Megafauna was conducted in the summer of 2018 to determine the distribution and abundance of cetaceans within the coastal and offshore waters of Canada. Tom Norris led the passive acoustic monitoring operations for this survey, and his Bio-Waves team of bioacousticians conducted 24-hour linear towed hydrophone array monitoring. Ocean Science Analytics worked in partnership with Bio-Waves, Inc. to conduct a post-processing analysis of this dataset and obtain an acoustic-based abundance estimate of porpoises by localizing individual click trains. Data were first re-processed in PAMGuard to reduce confounding noise and improve click classification. High-pass filters were used to reduce noise, and narrow-band, high-frequency (NBHF) echolocation clicks were up-sampled to improve the time delay measurement. The average peak frequency of clicks was used to determine their likely categorization as Dall’s (Phocoenoides dalli) or harbour (Phocoena phocoena) porpoise. Of the 90 NBHF clicks trains detected, 78 were categorized as Dall’s porpoise, and 56 yielded localizations. The resulting acoustic-based Dall’s porpoise abundance estimate (3967, CV 29%) was considerably lower than the visual-based estimate (27 002, CV 23%). We discuss possible reasons for this discrepancy and new insights into porpoise occurrence and distribution within the study area.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".