The smell of success: Reproductive success related to rub behavior in brown bears
Bibliographic record
Abstract
Several species of bears are known to rub deliberately against trees and other objects, but little is known about why bears rub. Patterns in rubbing behavior of male and female brown bears (Ursus arctos) suggest that scent marking via rubbing functions to communicate among potential mates or competitors. Using DNA from bear hairs collected from rub objects in southwestern Alberta from 2011-2014 and existing DNA datasets from Montana and southeastern British Columbia, we determined sex and individual identity of each bear detected. Using these data, we completed a parentage analysis. From the parentage analysis and detection data, we determined the number of offspring, mates, unique rub objects where an individual was detected, and sampling occasions during which an individual was detected for each brown bear identified through our sampling methods. Using a Poisson regression, we found a positive relationship between bear rubbing behavior and reproductive success; both male and female bears with a greater number of mates and a greater number of offspring were detected at more rub objects and during more occasions. Our results suggest a fitness component to bear rubbing, indicate that rubbing is adaptive, and provide insight into a poorly understood behaviour.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".