Animal Vehicle Collision Reduction and Habitat Connectivity Pooled Fund Study – Literature Review
Bibliographic record
Abstract
This report contains a summary of past research and new knowledge about the effectiveness of mitigation measures aimed at reducing animal-vehicle collisions and at providing safe crossing opportunities for wildlife. The measures are aimed at terrestrial large bodied wild mammal species, free roaming large livestock species (e.g. cattle, horses), free roaming large feral species (e.g. “wild” horses and burros), and small animal species (amphibians, reptiles, and small mammals). While mitigation is common, it is best to follow a three-step approach: avoidance, mitigation, and compensation or “off-site” mitigation. If reducing collisions with large wild mammals is the only objective, the most effective measures include roadside animal detection systems, wildlife culling, wildlife relocation, anti-fertility treatments, wildlife barriers (fences),and wildlife fences in combination with wildlife crossing structures. If the objectives also include maintaining or improving connectivity for large wild mammals, then wildlife barriers (fences) in combination with wildlife crossing structures are most effective. Measures for large domestic mammal species are largely similar, though for free roaming livestock there are legal, moral and ethical issues. For small animal species, temporary or permanent road closure and road removal are sometimes implemented, but barriers in combination with crossing structures are the most common.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".