Wildlife friendly fence designs and elk fence crossing behavior
Bibliographic record
Abstract
Abstract Fencing is a ubiquitous feature of our agricultural landscape. Fences necessarily have the potential to reduce habitat connectivity for resident ungulate populations. Unsuccessful fence crossings have the potential to cause injury or death to wildlife, as well as resulting in damage to the fence in terms of time and maintenance costs. Wildlife friendly fence designs may provide landowners and ungulate managers the opportunity to mitigate risks associated with wildlife crossings. Using remote cameras ( n = 12) along the perimeter fence of the Wainwright Dunes Ecological Reserve, Alberta, we quantified and compared elk crossing behaviors at standard 4 strand fences and gates as well as 3 strand fences and gates both with experimentally modified top and bottom strand heights. We found that wildlife friendly designs promoted behavioral options for elk of various demographic classes to cross fences. Our results suggest that the number of strands and the height of the top and bottom strand are important determinants for animals deciding to cross over, through, or under fences. While difficult or problematic crossings were primarily determined by how the individual crossed and made up a proportionally small number of crossings, the sheer volume of crossings we observed suggests that any modification which increases fence permeability to elk will likely result in less damage to fences and the crossing individuals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".