Geographic Distribution of North Atlantic Humpback Whales (Megaptera novaeangliae) with Fluke Scars Caused by Killer Whales (Orcinus orca)
Bibliographic record
Abstract
Although killer whale (Orcinus orca) predation on humpback whales (Megaptera novaeangliae) is rarely witnessed, resultant scars on humpback flukes provide evidence of non-lethal interactions. Humpback whale photo-identification catalogs from the North Atlantic were used to evaluate humpback flukes (n = 10,957) for the presence and severity of killer whale scarification (e.g., rake marks, teeth indentations, missing tissue). Flukes were coded as none, light, moderate, or severe based on the extent of scarring. Even with increased sample sizes, especially for Norway and Iceland, the distribution of high-latitude humpbacks with killer whale scarring was consistent with prior studies: Atlantic Canada (21.7%), West Greenland (15.5%), Gulf of Maine (13.5%), Iceland (combined with East Greenland, 9.3%), and Norway (7.9%). For the first time, scarring rates are presented based on data from Ireland, Scotland, and England (11.0%) as well. Scarring frequencies generally differed between the eastern and western North Atlantic despite the co-occurrence of all migrating humpbacks in low-latitude breeding grounds, suggesting the occurrence of killer whale interactions in the distinct feeding grounds or along northward migration routes. While it was not possible to determine exactly where these interactions took place, the likelihood of a killer whale encounter and subsequent scar acquisition was greatest for humpbacks that feed in Atlantic Canada.
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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 | 0.000 |
| 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".