Effects of linear features on resource selection and movement rates of wood bison (<i>Bison bison athabascae</i>)
Bibliographic record
Abstract
Human-mediated disturbances can lead to novel environmental features that can affect native biota beyond simple habitat loss. In boreal forests of western Canada, linear features (LFs; e.g., pipelines, seismic lines, and roads) are known to alter behaviour, movements, and interactions among species. Understanding LF impacts on native species has therefore been a management priority. Here, we investigate how LFs affect the spatial behaviour of wood bison (Bison bison athabascae Rhoads, 1898), which are designated as “threatened” in Canada. Using data collected from the Ronald Lake population in northeastern Alberta, we assessed how LFs influenced habitat selection and movement of bison by testing support among three hypotheses explaining whether LFs (i) increased forage availability, (ii) enhanced movement efficiency, or (iii) increased predation risk. Results supported the movement efficiency hypothesis as bison were generally ambivalent toward LFs, showing weak selection or avoidance depending on land-cover type, but moved slightly faster when on them. These findings contrast with avoidance behaviours reported for sympatric woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)), which are also “threatened.” Our results should inform critical habitat decisions for wood bison, but we caution that further research is needed to understand the effects of LFs on bison demography.
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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 | 0.001 | 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) | 0.002 | 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".