Call discrimination for an unknown pod of killer whales (<i>Orcinus orca</i>) in the Eastern Canadian Arctic
Bibliographic record
Abstract
Killer whales produce pulsed calls, which are used for communication. Calls are highly stereotyped, and repertoires are unique to individual pods. Discrimination amongst these calls and comparison of call repertoires between pods can help determine population structure in killer whales and can be used to track pod movements. Calls were detected in underwater acoustic recordings from August to September 2017 in the waters near the community of Pond Inlet, Nunavut, Canada. Eight stereotypic call types were identified using whistle contour extraction and network analysis to compare contours. We present a repertoire of killer whale calls recorded. The potential for increased killer whale presence and magnitude of predation on narwhals is a source of concern for management of the population and by Inuit subsistence hunters who rely on narwhals for food and economic benefit. Describing the acoustic repertoire of killer whales seasonally present in the Canadian Arctic may help identify the stock or pod and determine their seasonal movements. Comparisons of this repertoire with killer whale calls from other Atlantic pods has not yet yielded a match. However, the results presented may provide a basis for future comparisons and aid in identifying killer whale ecotypes 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".