SPATIAL RESPONSES OF WOLVES TO ROADS AND TRAILS IN MOUNTAIN VALLEYS
Bibliographic record
Abstract
Increasing levels of human activity in mountainous areas have high potential to inhibit animal movement across and among valleys. We examined how wolves respond to roads, trails, and other developments. We recorded the movements of two wolf packs for two winters by following their tracks in the snow and simultaneously recording positions with a hand‐held global positioning system. We then used matched case‐controlled logistic regression to compare habitat covariates of wolf paths (cases) to multiple paired random locations (controls). This analysis emphasized the differences within pairs of cases and controls, rather than differences in their overall distribution, making it useful to assess fine‐scale habitat selection and path data. Both packs selected low elevations, shallow slopes, and southwest aspects. They selected areas within 25 m of roads, trails, and the railway line and more strongly selected low‐use roads and trails compared to high‐use roads and trails. One pack strongly avoided distances between 26 and 200 m of high‐use trails; otherwise, the wolves weakly selected or avoided this distance class. Both packs avoided areas of high road and trail density. We concluded that roads and trails have a cumulative effect on wolf movement and that management of trails, in addition to roads, may be needed to retain high‐quality habitat for wolves, particularly in known movement corridors.
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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 | 0.002 |
| 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".