Bibliographic record
Abstract
This chapter on reducing wildlife-vehicle collisions is from a book on highways, wildlife, and habitat connectivity. The authors describe 41 different types of mitigation measures or different combinations of mitigation measures aimed at reducing collisions with large, wild ungulates in the United States and Canada, including deer. For each measure, they discuss the estimated effectiveness. Some of the measures (n = 16) are aimed at influencing driver behavior and some (n = 25) at influencing animal behavior. Mitigation measures aimed at influencing driver behavior include public information and education, various types of permanent warning signs, seasonal warning signs, animal detection systems, measures that increase the visibility for drivers, measures that reduce traffic volume, temporary road closures, reduced vehicle speed, and wildlife crossing assistants. Mitigation measures aimed at influencing animal behavior include measures directed at scaring ungulates away from the road and road corridor, alerting them to approaching traffic, reducing the attractiveness of the road or road sides, increasing the attractiveness of areas away from the road, providing them with a resting area when crossing multiple lanes of traffic, pathways that allow animals to escape from the road corridor, population size reduction efforts, physical barriers that keep animals off the road, and elevating or tunneling roads. The authors conclude that long bridges and tunnels, wildlife fencing in combination with underpasses and overpasses, and animal detection systems, with or without wildlife fencing, are among the most effective measures to reduce collisions with large ungulates.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.042 | 0.007 |
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".