Wildlife Exclusion Systems for Accident Mitigation on British Columbia Highways
Bibliographic record
Abstract
The British Columbia Ministry of Transportation (BCMoT) has been addressing the issue of motor vehicle-related wildlife mortality on Provincial highways with wildlife exclusion fencing and related engineered structures since the 1980's. As a result, British Columbia wildlife are protected by the most extensive network of wildlife exclusion systems constructed by a transportation agency in North America. The BCMoT wildlife exclusion infrastructure consists of over 470 km of wildlife exclusion fencing complete with crossing structures designed to: • protect the motoring public and wildlife; • maintain operational efficiency of highways; and • ensure wildlife habitat connectivity. Wildlife exclusion systems are typically incorporated as an integral part of new highway construction to address projected potential wildlife mortality. As part of BCMoT's environmental assessment process, extensive wildlife identification and monitoring programs conducted by professional biologists and wildlife experts commence years before highway construction begins. When wildlife population clusters and migration routes are identified during environmental assessments, the habitat fragmenting potential of wildlife exclusion fencing is reduced with crossing structures. In some case, wildlife exclusion systems are retrofitted on existing highways where problematic wildlife accident locations which have developed over time are identified using BCMoT's Wildlife Accident Reporting System (WARS). With each successive project, BCMoT has refined its fence and crossing structure designs and standards to increase the efficiency and effectiveness of its wildlife exclusion systems as the movement patterns and behavior of wildlife are better understood. BCMoT's approach to reducing the potential for wildlife mortality has evolved from the application of simple engineered structures into more comprehensive integrated wildlife management systems as the knowledge about the dynamics of the highway/wildlife habitat interface in British Columbia has grown.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".