Reducing Wildlife Collisions: What Is Working in Northeastern Ontario
Bibliographic record
Abstract
Wildlife/vehicle collisions pose a serious safety risk for motorists across Canada, and they are increasing annually. In Ontario alone, there are approximately 14 000 wildlife/vehicle collisions reported each year, with many more unreported. In Northeastern Ontario, wildlife collisions are even more frequent, and can account for as high as 50% of the total number of collisions along some highways. This paper describes the mitigation efforts on two major highways (Highways 11 and 69) over the past few decades. Prior to the last decade, wildlife collision reduction efforts in Ontario were primarily limited to installing wildlife warning signs and no discernable reduction in wildlife collisions was observed. In 2005, the Ontario Ministry of Transportation (MTO), Northeastern Region, commenced a more proactive approach to reducing wildlife/vehicle collisions by installing emerging mitigation methods such as crossing structures and fencing on both Highway 11 and 69. To date, the most extensive mitigation in Ontario is on Highway 69 between Parry Sound and Sudbury, where highway expansion and upgrades are currently being completed and one wildlife overpass, one underpass, twenty-seven one-way gates, two texas gates, and 10 km of fencing have been installed. In September 2011, mitigation effectiveness monitoring was initiated on this section of highway. Key results have shown that more species are using the wildlife overpass over time and that most animals, such as Moose and Deer prefer the wildlife overpass to the wildlife underpass. Preliminary data has shown a reduction in wildlife/vehicle collisions in the fenced section, and no Moose and Elk have breached the fencing system. Long-term monitoring is required to assess overall effectiveness of the crossing structure and fencing systems for all wildlife populations in the study area. Monitoring efforts are ongoing and are expected to produce additional results prior to the 2014 TAC Conference, such as an assessment of black bear population-level use of wildlife crossings through DNA analysis conducted on hair and scat samples.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".