Road characteristics best predict the probability of vehicle collisions with a non-native ungulate
Bibliographic record
Abstract
With roads encroaching into natural environments, there is an increased likelihood of wildlife coming into contact with vehicles, resulting in an increase in wildlife–vehicle collisions. Our goal was to investigate environmental correlates of moose–vehicle collisions (MVCs) on the island of Newfoundland, Canada. We developed predictive models to compare environmental variables at known MVC locations with environmental variables at random sites along the Newfoundland road network. The most supported generalized linear model explained ~36% of the variance in the probability of MVC occurrence. This top model predicted an increase in the probability of MVC occurrence: with decreasing distance to developed areas; on primary rather than secondary roads; on straight rather than curved roads; and in locations where roadside vegetation cutting has occurred. Our study highlights MVC predictors that are consistent with other wildlife–vehicle collision studies around the globe and which will serve as the basis for mitigation strategies on the island of Newfoundland with potential applications to other regions with high moose densities.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".