Bears napping nearby: daybed selection by brown bears (<i>Ursus</i> <i>arctos</i>) in a human-dominated landscape
Bibliographic record
Abstract
Daybeds are essential for the survival of brown bears (Ursus arctos L., 1758) and may represent a population-limiting resource in human-dominated landscapes. In this study, we demonstrate which land-cover types and bear characteristics affect daybed selection in north-central Slovakia. We used the positional and activity data of 21 bears acquired by GPS–GSM telemetry to identify 3864 daybeds. By use of K-select analysis and linear mixed-effects modelling, we explored how bears chose these places for their daytime resting. The most important drivers for daybed selection were the presence of dense regenerating forests and forest–shrubbery belts in farmland. Bears avoided resting in older forests without suitable undergrowth. Females selected daybeds differently depending on the presence of dependent cubs. During spring – early summer, females with cubs of the year avoided other bears by selecting more rugged terrain. These females also selected daybeds significantly closer to human settlements than adult males, possibly to avoid the risk of infanticide. In late summer – autumn, all bears selected daybeds closer to human settlements than in spring, probably because they were attracted by maize (Zea mays) fields and fruit trees. Many daybeds were located outside protected areas in farmland closer to people, which could increase bear–human conflicts.
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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 | 0.000 |
| 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".