Snow conditions influence grey wolf (<i>Canis</i> <i>lupus</i>) travel paths: the effect of human-created linear features
Bibliographic record
Abstract
Although travel in deep snow imposes high energetic costs, animals can mitigate these costs through behavioural adaptations. For example, they can select habitats with shallower or more supportive snow. It is less well known, however, if animals select for favourable snow conditions at the scale of the step, i.e., along the travel paths themselves. We snow-tracked grey wolves (Canis lupus L., 1758) over 187 km and used a paired design to compare snow conditions on travel paths to snow 1 m and 10 m away. Snow on travel paths was 3.2 cm shallower than measurements 1 m away, except when wolves travelled on linear features recently compacted by humans. In those cases, the mean difference in snow depth increased to 17.5 cm. Our analyses suggest that, under natural snow conditions, wolves are limited in the fine-scale differences they can achieve along their travel paths. By creating areas with highly favourable snow conditions, anthropogenic activities drastically change the winter landscape, with potential implications for energetics and predator–prey dynamics.
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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 | 0.001 | 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".