Vehicle traffic shapes grizzly bear behaviour on a multiple‐use landscape
Why this work is in the frame
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Bibliographic record
Abstract
Summary Roads cause functional habitat loss, alter movement patterns and can become ecological traps for wildlife. Many of the negative effects of roads are likely to be a function of the human use of roads, not the road itself. However, few studies have examined the effect of temporally and spatially varying traffic patterns on large mammals, which could lead to misinterpretations about the impact of roads on wildlife. We developed models of traffic volume for an entire road network in south‐western A lberta, C anada, and documented for the first time the response of grizzly bears U rsus arctos L to a wide range of traffic levels. Traffic patterns caused a clear behavioural shift in grizzly bears, with increased use of areas near roads and movement across roads during the night when traffic was low. Bears selected areas near roads travelled by fewer than 20 vehicles per day and were more likely to cross these roads. Bears avoided roads receiving moderate traffic (20–100 vehicles per day) and strongly avoided high‐use roads (>100 vehicles per day) at all times. Synthesis and applications . Grizzly bear responses to traffic caused a departure from typical behavioural patterns, with bears in our study being largely nocturnal. In addition, bears selected private agricultural land, which had lower traffic levels, but higher road density, over multi‐use public land. These results improve our understanding of bear responses to roads and can be used to refine management practices. Future management plans should employ a multi‐pronged approach aimed at limiting both road density and traffic in core habitats. Access management will be critical in such plans and is an important tool for conserving threatened wildlife populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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 it