Killer whales (<i>Orcinus orca</i>) in the Canadian Arctic: Distribution, prey items, group sizes, and seasonality
Bibliographic record
Abstract
Abstract Killer whales (Orcinus orca) have a global distribution, but many high‐latitude populations are not well studied. We provide a comprehensive review of the history and ecology of killer whales in the Canadian Arctic, for which there has previously been little information. We compiled a database of 450 sightings spanning over 15 decades (1850–2008) to document the historical occurrence, distribution, feeding ecology, and seasonality of killer whales observed throughout the region. Sighting reports per decade increased substantially since 1850 and were most frequent in the eastern Canadian Arctic. The mean reported group size was 8.3 (median = 4, range 1–100), but size varied significantly among regions and observed prey types. Observations of predation events indicate that Canadian Arctic killer whales prey upon other marine mammals. Monodontids were the most frequently observed prey items, followed by bowhead whales (Balaena mysticetus), phocids, and groups of mixed mammal prey. No killer whale sightings occurred during winter, with sightings gradually increasing from early spring to a peak in summer, after which sightings gradually decreased. Our results suggest that killer whales are established, at least seasonally, throughout the Canadian Arctic, and we discuss potential ecological implications of increased presence with declining sea ice extent and duration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".