A review of Canadian Arctic killer whale (<i>Orcinus orca</i>) ecology
Bibliographic record
Abstract
The killer whale (Orcinus orca (Linnaeus, 1758)) is a widely distributed marine predator with a broad ecological niche at the species level with evidence of specialization and narrow ecological niches among populations. Their occurrence in Canadian Arctic waters is limited by sea ice and it has been suggested that climate warming, which has caused increases in the area of ice-free water and duration of the ice-free season, has led to an increased killer whale presence during the open-water period. In this review, we summarize our knowledge of Canadian Arctic killer whale demographics and ecology, synthesizing published and previously unpublished information in a single document. More specifically, we summarize our knowledge of killer whale population size and trends, distribution and seasonality (including results from recent satellite-tracking studies), feeding ecology, and threats, and identify research priorities in the Canadian Arctic. Despite increased research efforts during the past decade, our demographic and ecological knowledge remains incomplete. An improved ecological understanding is necessary for effective management of killer whales and their prey, species of ecological, economic, and cultural importance to Canadian Inuit and the marine ecosystem. This knowledge will allow us to better understand the ecological consequences of a changing Arctic climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".