Patterns and temporal trends in canine breakage and scarring in Western Hudson Bay polar bears (Ursus maritimus)
Bibliographic record
Abstract
Canines are used by carnivores for prey capture and social interactions but are often damaged. The highly carnivorous polar bear (Ursus maritimus) has a female defence polygyny mating system where males compete for access to females and injuries to males, such as broken canines and cuts, are common. The Western Hudson Bay polar bear subpopulation has declined in abundance in recent decades and shifted from a female-biased to a male-biased adult sex ratio, which may have affected their mating system. We hypothesize that if changes in subpopulation structure have affected the mating system, then canine breakage and scarring may have changed over time. We assessed age- and sex-specific occurrences of canine breakage and scarring in 3493 individuals between 1981-2023 using non-parametric statistical analyses and linear mixed effect models. We found age- and sex-related differences in mean values of breakage and scarring. These injury occurrences increased with age in both sexes and males had greater amounts of both breakage and scarring compared to females. As the only main effect, sampling year was significant and indicated increasing breakage in both sexes over time; however, the top breakage model did not include year, indicating it was not as strong a predictor of breakage and scarring as age or sex. Age, sex, and year were all in the top model for predicting maximum scarring probabilities. We found some evidence that injuries changed over time, suggesting there could be changes to intraspecific interactions, but additional monitoring is needed.
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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 | 0.000 | 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".