A follow‐up assessment of wildlife‐permeable fences used in the reintroduction of bison
Bibliographic record
Abstract
Fences are an important tool for anchoring reintroduced species to a target area, and there is a need to understand their effect on other wildlife species. However, little is understood about the response of wildlife to newly constructed fences over time. We evaluated fences used in the reintroduction of plains bison Bison bison to Banff National Park, Canada. These fences were designed to contain reintroduced bison while allowing for the free passage of other wildlife. In 2020, we provided an assessment of the permeability of several fence designs. Here, we investigated longer‐term fence effects and addressed the emerging question of whether wildlife adapt their behaviours to navigate fences more effectively over time. We used an expanded array of remote cameras and a before‐after‐control‐impact design to evaluate changes in detection probability for 12 species. Next, we tested for changes in crossing rates and travel speeds of migratory elk Cervus canadensis using 22 years of GPS collar data. Finally, we examined whether species detections or elk movements changed over time after fences were constructed. Changes in detection probability near fences were inconsistent between species. Elk fence crossing rates decreased after fence construction, and travel speeds slowed by a negligible amount. However, these effects were temporary – wildlife learned to cross fences more efficiently over time. Elk movement metrics followed a non‐linear pattern after the appearance of fences and began returning to pre‐fence states after approximately two years. Our study provides new information on the implementation of fences for conservation objectives while minimizing impacts on sympatric wildlife.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".