Room without a view – den construction in relation to body size in brown bears
Bibliographic record
Abstract
Abstract Hibernation is an adaptive strategy to survive harsh winter conditions and food shortage. The use of well-insulated winter dens helps animals minimize energy loss during hibernation. Brown bears ( Ursus arctos ) commonly use excavated dens for hibernation. Physical properties of excavated dens, such as the amount of space between a bear and the inner wall, wall/roof thickness, and bedding materials, are expected to impact heat retention and energy conservation of bears. The objective of this study was to examine the impact of physical properties of excavated dens on energy conservation in hibernating bears. Our hypothesis was that bears excavate dens in a way to minimize heat loss and optimize energy conservation during hibernation. We predicted that physical properties of excavated dens would significantly affect the bears’ post-hibernation body condition. To test our hypothesis and prediction, we analyzed data collected from brown bears in Sweden with linear mixed effects models, examining (i) what factors affect den-excavation behavior and (ii) if physical properties of excavated dens affect post-hibernation body condition. We found that bears excavated a den cavity in relation to their body size, that older bears tended to excavate better-fitting den cavities compared to young bears, and that the physical properties of excavated dens did not significantly affect a bears’ post-hibernation body condition. Older bears excavated better-fitting den cavities, suggesting a potentially experience-based shift with age in den-excavation behavior and an optimum cavity size relative to a bear’s body size. The strong year effect shown by the most parsimonious model for post-hibernation body condition suggests that variations in physical properties of excavated dens are possibly negligible, compared to the large annual variations in biotic and abiotic factors affecting pre-hibernation body condition and heat loss during hibernation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".