Divergent killer whale populations exhibit similar acquisition but different healing rates of conspecific scars
Bibliographic record
Abstract
Abstract Scars obtained from interactions with conspecifics may be caused by both playful and aggressive activities, making them useful when studying cetacean behaviour. This study investigates the effects of age and sex on conspecific scar acquisition and healing in three genetically distinct populations of killer whales (Orcinus orca) each with unique diets and social structures. The sample consisted of 50 of the most commonly photo-identified individuals from all sex and age classes in each of the Bigg’s, Northern Resident, and Crozet killer whale populations. The number of new scars annually acquired by an individual as well as how long it took them to disappear were extracted from annual photo-identification images of these individuals taken between the years of 2008 and 2021. Scar acquisition was analysed using a generalized additive model while scar healing was assessed using Kaplan-Meier survival curves. Results showed an inverse relationship between scar acquisition and age, as well as an effect of sex with males being more scarred than females amongst all age classes. No significant differences in scar acquisition between populations was found. Scar re-pigmentation was faster in Northern Residents compared to Crozet and Bigg’s individuals and varied amongst age classes, with scars on calves and juveniles disappearing more quickly than those on adults. These population- and age-based differences in healing may be due to scar severity, while results around scar acquisition suggest that the nature of physical interactions between sex and age classes in this species are homogenous despite cultural and genetic differences that have evolved between populations.
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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".