A before-after-control-impact study of wildlife fencing along a highway in the Canadian Rocky Mountains
Bibliographic record
Abstract
Wildlife exclusion fencing has become a standard component of highway mitigation systems designing to reduce collisions with large mammals. Past work on the effectiveness of exclusion fencing has relied heavily on control-impact (i.e., space-for-time substitutions) and before-after study designs. These designs limit inference and may confound the effectiveness of mitigation with co-occurring process that also change the rate of collisions. We used a replicated before-after-control-impact study design to assess fencing effectiveness along the Trans-Canada Highway in the Rocky Mountains of Canada. We found that collisions declined for common ungulates species (elk, mule deer and white-tailed deer) by up to 96% but not for large carnivores. The weak response of carnivores is likely due to combination of fence intrusions and low sample sizes. When accounting for background changes in collision rates observed at control sites, naïve estimates of fencing effectiveness declined by 6% at one site to 90% and increased by 10% at another to a realized effectiveness of 82%. When factoring in the cost of ungulate collisions to society as a whole, fencing provided a net economic gain within 1 year of construction. Over a 10-year period, fencing would provide a net economic gain of >$500,000 per km in reduced collisions. In contrast, control site may take upwards of 90 years before the background rates of collisions decline to a break even point. Our study highlights the benefits of long-term monitoring of road mitigation projects and provides evidence of fencing effectiveness for reducing wildlife-vehicle collisions involving large mammals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".