Road visibility influences habitat selection by grizzly bears (<i>Ursus arctos horribilis</i>)
Bibliographic record
Abstract
Anthropogenic disturbances, including roads, are known to influence animal habitat selection and mortality. In this study, we consider the role of sensory perception in understanding why and how animals respond to disturbances. Our goal was to investigate the effect of visual perception (visibility) around roads on grizzly bear (Ursus arctos horribilis Ord, 1815) habitat selection and mortality in Alberta, Canada. We used detailed topographic and vegetation data from airborne light detection and ranging (lidar) to estimate visibility around roads. We modelled habitat selection as a function of road visibility and environmental variables using GPS telemetry data from 39 grizzly bears and integrated step selection analysis (iSSA). Finally, we assessed mortality risk in visible areas by comparing habitat selection between grizzly bears that died and those that survived. We found that grizzly bears were less likely to select visible areas when moving slowly or resting, but were more likely to select visible areas when travelling. We found that grizzly bears that survived selected for areas farther from roads than grizzly bears that died. However, no difference in selection for visible areas was observed. An exploratory analysis showed that grizzly bear mortalities commonly occurred in visible areas. Our findings highlight the importance of sensory perception in understanding animal 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 | 0.000 | 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".