Determining the influence of snow and temperature on the movement rates of wood bison (<i>Bison bison athabascae</i>)
Bibliographic record
Abstract
Snow is understood to limit wildlife movements, often being the most important determinant of winter movement for animals in the boreal forest. However, the combined effect of snow and temperature on the movement ecology of animals at high latitudes is less understood. Here, we used GPS-collar data from a small population of wood bison (Bison bison athabascae Rhoads, 1898) in northeastern Alberta, Canada, to develop a series of generalized additive mixed models characterizing the effect of cumulative snow depth, daily change in snow depth, and temperature on movement rates. Our most supported model included cumulative snow depth, temperature, and day of winter. Bison movements decreased in the first 75 days of winter during snow accumulation and dramatically increased in the final 14 days of winter during snow melt. Cumulative snow depth, not daily change in snow depth, reduced wood bison movement rates, and movement rates increased more rapidly in warmer temperatures than in temperatures below −6.4 °C. By quantifying both the direction and the magnitude of snow and temperature’s effects on bison movement, our study fills critical knowledge gaps relating to the winter movement ecology of wood bison and contributes to a growing body of knowledge informing their conservation in the Anthropocene.
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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 | 0.001 | 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".