Identifying attributes associated with brown bear (<i>Ursus arctos</i>) road-crossing and roadkill sites
Bibliographic record
Abstract
Habitat fragmentation caused by transportation infrastructure is an issue of growing concern worldwide. We show how secondary roads may affect landscape permeability for brown bears (Ursus arctos Linnaeus, 1758). We focused on identifying environmental variables that govern the selection of road-crossing zones by bears (crossing model). We also investigated whether variables that characterize road-crossing zones differ from those that are typical for bear–vehicle collision sites (collision model). The study area was located in north-central Slovakia. To identify road-crossing sites, we used the GPS fixes of 27 bears and identified 35 bear–vehicle collision sites from a different data set. We used mixed-effects logistic regression to model resource selection at road-crossing sites and to compare bear-crossing sites with bear-kill sites. The crossing model showed that the traffic volume with distance to forest and grassland were the most influential factors in bear selection of road-crossing sites. Results of the collision model indicated that successful road crossings by bears were located at different road sections from vehicle collisions, which differed by a traffic volume of 5000 vehicles/24 h. The outcomes of this study can facilitate improved mitigation measures on secondary roads.
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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".