Animal Vehicle Collision Reduction and Habitat Connectivity Cost Effective Solutions - Final Report
Bibliographic record
Abstract
Wildlife-vehicle collisions (WVCs) are a significant component of overall crashes in the US and Canada. Roads and their traffic also create partial or total barriers to the movement of wildlife, both large and small. There are several well-studied proven mitigation measures that significantly reduce WVCs, provide for safe animal passage across roads, and maintain habitat connectivity. Highly effective measures, such as overpasses and underpasses with fencing can reduce large animal WVCs by over 80% – 100% on average; yet these structures can be costly and there is room for improvement in their design, the use of new materials, adding elements that improve their use by smaller animal species, such as reptiles and amphibians and improving their cost effectiveness. This Transportation Pooled Fund Study, TPF-5(358) (TPF Study), allowed researchers to evaluate the latest information on the effectiveness of 24 different highway mitigation measures designed to decrease collisions with large wildlife, large feral and domestic animals. Also reviewed were these same measures’ ability to protect small mammals, reptiles, and amphibians from collisions. The TPF Study also explored the effectiveness of the 24 measures ability to maintain or enhance habitat connectivity. It conducted 11 different research projects that variously explored a) the costs and benefits of animal-vehicle collisions and the mitigation measures that seek to reduce them, b) the ecological effectiveness of various mitigation measures, and 3) new designs for crossing structures for a variety of species. The project developed a manual of best practices and concluded with a final report.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".