Killer Whale (Orcinus orca) Pulsed Calls in the Eastern Canadian Arctic
Bibliographic record
Abstract
Killer whales (Orcinus orca) produce a variety of acoustic signal types used for communication: clicks, whistles, and pulsed calls. Discrete pulsed calls are highly stereotyped, repetitive, and unique to individual pods found around the world. Discriminating amongst pod specific calls can help determine population structure in killer whales and is used to track pod movements around oceans. Killer whale presence in the Canadian Arctic has increased substantially, but we have limited understanding of their ecology, movements, and stock identity. Two autonomous passive acoustic monitoring (PAM) hydrophones were deployed in the waters of Eclipse Sound and Milne Inlet, in northern Baffin Island, Nunavut, Canada, in August and September 2017. Eleven killer whale pulsed call types, three multiphonic and eight monophonic, are proposed and described using manual whistle contour extraction and feature normalization. Automated detection of echolocation clicks between 20 and 48 kHz demonstrated little to no overlap between killer whale calls and echolocation presumed to be narwhal, which suggests that narwhal remain audibly inconspicuous when killer whales are present. Describing the acoustic repertoire of killer whales seasonally present in the Canadian Arctic will aid in understanding their acoustic behaviour, seasonal movements, and ecological impacts. The calls described here provide a basis for future acoustic comparisons across the North Atlantic and aid in characterizing killer whale demographics and ecology, particularly for pods making seasonal incursions into Arctic waters.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".