Coping with human disturbance: spatial and temporal tactics of the brown bear (<i>Ursus arctos</i>)
Bibliographic record
Abstract
In human-dominated landscapes, species with large spatial requirements, such as large carnivores, have to deal with human infrastructure and activities within their home ranges. This is the case for the brown bear ( Ursus arctos L., 1758) in Scandinavia, which is colonizing more human-dominated landscapes, leading inevitably to an overlap between their home ranges and anthropogenic structures. In this study, we investigated fine-scale habitat selection by brown bears to examine how they deal with this potential disturbance. Using Global Positioning System (GPS) data, we studied (i) habitat selection of female brown bears within their home range and (ii) the influence of diurnal variation in human disturbance on fine-scale habitat use. As expected, females selected habitats within their home range that provided abundant food resources and minimized human-caused disturbance. In addition, our temporal analysis of habitat selection revealed an avoidance of disturbed areas and a selection of slopes by bears during periods of highest human activities, i.e., during daylight hours. We clearly demonstrate the importance of considering the fluctuations in human activity when studying habitat selection, especially at fine spatial scales. Failing to do so may considerably reduce the power to detect important fine-scale habitat-selection behaviors.
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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 | 0.000 | 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".