Grizzly bear movements relative to roads: application of step selection functions
Why this work is in the frame
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Bibliographic record
Abstract
Access management is among the most important conservation actions for grizzly bears in North America. In Alberta, Canada, nearly all grizzly bear mortalities are caused by humans and occur near roads and trails. Consequently, understanding how bears move relative to roads is of crucial importance for grizzly bear conservation. We present the first application of step‐selection functions to model habitat selection and movement of grizzly bears. We then relate this to a step‐length analysis to model the rate of movement through various habitats. Grizzly bears of all sex and age groups were more likely to select steps closer to roads irrespective of traffic volume. Roads are associated with habitats attractive to bears such as forestry cutblocks, and models substituting cutblocks for roads outperformed road models in predicting bear selection during day, dawn, and dusk time periods. Bear step lengths increased near roads and were longest near highly trafficked roads indicating faster movement when near roads. Bear selection of roads was consistent throughout the day; however, time of day had a strong influence over selection of forest structure and terrain variables. At night and dawn, bears selected forests of intermediate age between 40 and 100 yr, and bears selected older forests during the day. At dawn, bears selected steps with higher solar radiation values, whereas, at dusk, bears chose steps that were significantly closer to edges. Because grizzly bears use areas near roads during spring and most human‐caused mortalities occur near roads, access management is required to reduce conflicts between humans and bears. Our results support new conservation guidelines in western North America that encourage the restriction of human access to roads constructed for resource extraction.
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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 | 0.001 |
| 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 | 0.001 |
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