Bibliographic record
Abstract
Wildlife collisions on Ontario's highways are an increasing problem. It is estimated that each year, approximately 14,000 (6%) of Ontario's vehicle collisions involve wildlife. Approximately 10% of these occur in Northeastern Region with an estimated cost of $110 million per year. The science of wildlife-vehicle collision mitigation is emerging and as such trials are still being completed to demonstrate the effectiveness of various mitigation strategies and techniques in specific terrain and conditions. In addition, the collection of wildlife collision data is based on reported collisions and does not include more minor collisions or all collisions with large commercial traffic. The aforementioned factors present challenges in addressing wildlife-vehicle collisions, particularly when the implementation of mitigation is costly. Northeastern Region MTO has struck a Wildlife Mitigation Team to begin to systematically address priority wildlife collision locations. The group has taken several approaches to addressing collisions which include strengthening data collection and analysis, provision of grade-separated crossing opportunities, installation of wildlife fencing, installation of wildlife reflectors and plans for the use of technologies such as the Radio-Activated Detection System or similar. Included in these efforts is a Sharepoint website accessible to the environmental function where experiences with mitigation techniques can be documented. The purpose of this paper is to present the efforts to date and experiences of the Wildlife Mitigation Team, including challenges encountered and lessons learned. This project was nominated for the TAC 2008 Environmental Achievement Award.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".