Planning and prioritization strategies for phased highway mitigation using wildlife‐vehicle collision data
Bibliographic record
Abstract
Abstract Mitigation measures to reduce wildlife‐vehicle collisions (WVCs) on highways are becoming an established practice in many jurisdictions. Most highway mitigation projects occur while roads are being upgraded, enlarged or repaired. Many smaller highways may not be subject to these types of upgrades in the near future but are nonetheless problematic for causing WVCs. Thus, it is important to find cost effective ways to locate and prioritize stretches of highway for mitigation. We present several criteria that can be used to assist in prioritizing the location of wildlife‐proof fencing along a 94‐km stretch of road in one of Canada's National Parks. We considered temporal consistency of WVC occurrences, conservation value (i.e. reduction in WVC rates), economic benefits (i.e. cost of mitigation vs benefits in WVC reduction), and a combined approach to prioritize management actions. We compared the efficacy of four different lengths of fencing (i.e. phase lengths) at meeting these criteria: 2 km, 5 km, 10 km and 25 km. We used 1,244 WVC records from a long‐tem monitoring program (1981‐2005) as data to assess mitigation effectiveness. We found that longer fences best address conservation concerns, but all fencing sections, irrespective of length, rarely captured > 50% of WVC locations by species. We found that shorter fences were more economically efficient, but also more variable in performance, than longer fences. Lastly, we found that longer fence lengths tend to produce the best results for the combined metric criteria. Clearly defined management goals will determine the extent to which a phased approach to highway mitigation is viable.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| 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) | 0.004 | 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".