Differential rates of killer whale attacks on humpback whales in the North Atlantic as determined by scarification
Bibliographic record
Abstract
As in other populations of killer whales, Orcinus orca, prey selectivity in the North Atlantic population may indicate behaviourally or ecologically distinct types of killer whales. Some killer whale ecotypes are known to prey on large whales, but the ecological impact of such predation events is unknown. Since killer whale attacks on humpback whales, Megaptera novaeangliae, are rarely witnessed, resultant scars may be used to determine the frequency of non-fatal predatory interactions. Using images from the North Atlantic Humpback Whale Catalogue (NAHWC), we examined humpback whale flukes for the presence of rake marks from killer whales (N = 5040). Scarring frequencies range from 2.7 to 17.4% and differ significantly among five regions of the North Atlantic (Gulf of Maine, Canada, West Greenland, Iceland and Norway). The scarring rate in the Canada region is significantly higher than all other regions, and Norway has a significantly lower scarring rate than all other regions, despite more frequently reported killer whale sightings in that region. Within the western North Atlantic, Canada has a scarring rate nearly twice that of either the Gulf of Maine or West Greenland. These data may reflect differential prey choice among killer whale ecotypes and/or the distribution of specific ecotypes across the North Atlantic basin.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".