Do the presence of culverts and their attributes influence the spatial distribution of roadkill along Autoroute 10 and Route 112 in Southern Québec?
Bibliographic record
Abstract
Roads impact wildlife by fragmenting habitats, impeding movement, and though wildlife-vehicle collisions (WVCs). Proven mitigation strategies include wildlife crossing structures, but wildlife also utilize existing water culverts and underpasses to cross roadways. They offer opportunities for retrofitting when budget constraints limit the feasibility of wildlife passages. This study investigates the spatial relationship between roadkill distribution and culvert presence along a four-lane divided freeway (A10) and a two-lane regional highway (R112) in Québec, Canada. I evaluate how culvert structural characteristics (e.g., materials, shapes, functions, and dimensions) and surrounding environmental features influence roadkill frequencies separately for amphibians, mammals, and reptiles. I address three research questions: (1) Does the presence of culverts influence the spatial distribution of roadkill? (2) Which structural and environmental attributes are associated with higher or lower roadkill frequencies for different animal groups? (3) What are the similarities and differences between R112 and A10? The findings provide strong evidence for culvert influence on roadkill patterns. Mammal and reptile fatalities were more frequently associated with dry passages, amphibians with wet passages, and all groups showed a preference for using concrete structures over metal or plastic and for larger culvert openings. The results support the installation of wildlife exclusion fencing to reduce roadkill and improve driver safety. Moving forward, this study may serve as the first phase of a Before-After-Control-Impact (BACI) study, the second phase should evaluate the effectiveness of retrofitted culverts with added wildlife fencing to produce high-inference results for evidence-based WVC mitigation strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".