Killer whale abundance and predicted narwhal consumption in the Canadian Arctic
Bibliographic record
Abstract
Range expansions and increases in the frequency of killer whale (Orcinus orca) sightings have been documented in the eastern Canadian Arctic, presumably the result of climate change-related sea-ice declines. However, the effects of increased predator occurrence on this marine ecosystem remain largely unknown. We explore the consequences of climate change-related range expansions by a top predator by estimating killer whale abundance and their possible consumptive effects on narwhal (Monodon monoceros) in the Canadian Arctic. Individual killer whales can be identified using characteristics such as acquired scars and variation in the shape and size of their dorsal fins. Capture-mark-recapture analysis of 63 individually identifiable killer whales photographed between 2009 and 2018 suggests a population size of 163 ± 27. This number of killer whales could consume >1,000 narwhal during their seasonal residency in Arctic waters. The effects of such mortality at the ecosystem level are uncertain, but trophic cascades caused by top predators, including killer whales, have been documented elsewhere. These findings illustrate the magnitude of ecosystem-level modifications that can occur with climate change-related shifts in predator distributions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".