Best Practices Manual to Reduce Animal-Vehicle Collisions and Provide Habitat Connectivity for Wildlife
Bibliographic record
Abstract
The goal for this manual is to provide practical information for the implementation of mitigation measures that aim to: 1. Improve human safety through reducing collisions with large animals, including large wild mammal species, select free roaming large feral species, and select free roaming large livestock species, and 2. Improve or maintain habitat connectivity for terrestrial wildlife species and selected feral species through safe crossing opportunities. This manual does not include all possible measures that can or may reduce animal-vehicle collisions and maintain or improve habitat connectivity for wildlife. The measures included in this manual are: Barriers (fences) in combination with crossing structures (for large wild mammals and for small wild animal species), roadside animal detection system, Barriers (fences), Barriers (fences) in combination with crossing structures (for free roaming livestock), and culling, relocation, anti-fertility treatment, roadside animal detection systems, barriers (fences), and barriers (fences) in combination with crossing structures (for large feral mammal species such as feral horses and burros).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.081 |
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".