Designing a fence that enables free passage of wildlife while containing reintroduced bison: a multispecies evaluation
Bibliographic record
Abstract
Reintroductions of extirpated species are an important global conservation tool, yet can be challenging for wide‐ranging species. Fences that help anchor reintroduced species to a target area may have deleterious effects on other wildlife. Here we assessed the wildlife‐permeability of six bison drift fence designs at three spatial scales during the reintroduction of a wild herd of plains bison Bison bison to a 1200 km 2 wilderness area in Banff National Park, Canada. First, we used an array of remote cameras along fences to capture wildlife interactions for 12 species, and modelled crossing success, preferred crossing methods and age–sex class tendencies. Second, we investigated fence barrier effects on wildlife movement at the local scale using cameras that were in place before and after fence construction. Finally, we tested for changes in movement rates of migratory elk and resident wolves at the landscape scale using GPS collar telemetry. Our results point to a single fence design that maximizes permeability for several species with diverse crossing strategies, and can be adjusted to contain bison. Wildlife detections increased independently of fence construction in our broader study area. Fence construction did not affect wolf or elk movements and migration at a landscape scale even when fences were deployed to obstruct bison. Our study highlights the important role of wildlife permeable fences in the reintroduction of large mammals such as bison.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".