Bibliographic record
Abstract
Carnivores living in areas of deep snow face additional energy expenditures during winter owing to increased locomotory costs. Such costs may vary in function of snow depth and hardness (sinking depth of animal) and travel speed. We estimated energetic costs of locomotion through snow in wild coyotes (Canis latrans) using three coyote-sized domestic dogs (Canis familiaris) to develop regression models predicting heart rate (as surrogate for energy expenditure) in relation to sinking depth and travel speed. In the absence of snow, heart rates of dogs increased linearly with travel speed (R 2 = 0.24), whereas when snow was present, track sinking depth affected heart rate substantially more than did travel speed. To assess whether our results with domestic dogs could help explain the behaviour of wild coyotes, we snow-tracked coyotes in southeastern Quebec, Canada, during two winters. During a normal harsh winter, coyotes relied on artificially packed snow (snowmobile and animal trails) more than during a mild winter. Coyotes typically exerted a fine-scale selection for snow depth and hardness that effectively reduced their sinking depth by ~2 cm. We estimated that travelling over snow increased coyote heart rate by 4%–6% in comparison with locomotion on hard surfaces, whereas fine-scale selection saved a similar amount of extra energy. We hypothesize that the use of snow packed by anthropogenic activities, especially snowmobile trails, may not only facilitate coyote movements in deep snow environments but also allow occupation of marginal habitats such as forested areas of northeastern North America.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".