A before-after-control-impact study of wildlife fencing along a highway in the Canadian Rocky Mountains
Why this work is in the frame
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Bibliographic record
Abstract
Wildlife exclusion fencing has become a standard component of highway mitigation systems designed 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 changes the rate of collisions. We used a replicated ( n = 2 sites monitored for over 1000 km years combined) 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 the combination of fence intrusions and low sample sizes. We calculated realized fencing effectiveness by applying the same change in collision rates observed at control (unfenced) sites as the expected change for adjacent fenced sections. Compared with the apparent fencing effectiveness (i.e., the difference in WVCs rates before and after fencing was installed), the realized estimates of fencing effectiveness declined by 6% at one site and increased by 10% at another site. When factoring in the cost of ungulate collisions to society, 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. 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it