Perception of Human-Derived Risk Influences Choice at Top of the Food Chain
Bibliographic record
Abstract
On human-used landscapes, animal behavior is a trade-off between maximizing fitness and minimizing human-derived risk. Understanding risk perception in wildlife can allow mitigation of anthropogenic risk, with benefits to long-term animal fitness. Areas where animals choose to rest should minimize risk from predators, which for large carnivores typically equate to humans. We hypothesize that high human activity leads to selection for habitat security, whereas low activity enables trading security for forage. We investigated selection of resting (bedding) sites by GPS radiocollared adult grizzly bears (n = 10) in a low density population on a multiple-use landscape in Canada. We compared security and foods at resting and random locations while accounting for land use, season, and time of day. On reclaimed mines with low human access, bears selected high horizontal cover far from trails, but did not avoid open (herbaceous) areas, resting primarily at night. In protected areas bears also bedded at night, in areas with berry shrubs and Hedysarum spp., with horizontal cover selected in the summer, during high human access. On public lands with substantial human recreation, bears bedded at day, selected resting sites with high horizontal cover in the summer and habitat edges, with bedding associated with herbaceous foods. These spatial and temporal patterns of selection suggest that bears perceive human-related risk differentially in relation to human activity level, season and time of day, and employ a security-food trade-off strategy. Although grizzly bears are presently not hunted in Alberta, their perceived risks associated with humans influence resting-site selection.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".