Prioritizing human safety and multispecies connectivity across a regional road network
Bibliographic record
Abstract
Abstract The intersection of wildlife and people on roads raises two critical issues: the barrier and mortality effects of roads on wildlife and risks to people from animal‐vehicle collisions (AVCs). Road mitigation decisions are typically made at the discretion of transportation departments that are mandated to primarily address motorist safety. Therefore, prioritization of road sections for mitigation currently focuses on identification of spatial clusters of AVCs. We sought to understand if AVC clusters align with multispecies connectivity across roads to accurately identify multipurpose mitigation hotspots. We developed a decision‐support tool based on weighted priorities for mitigation planning across 7,900 km of roads over an 84,000‐km 2 area of southern Alberta, Canada. To assess alignment, we built functional connectivity models for four focal species (prairie rattlesnake, grizzly bear, mule deer, and pronghorn) and a species‐neutral structural connectivity model. We integrated AVC risk and wildlife connectivity indices into Mitigation Priority Indices that varied the weighting of individual indices. Our results demonstrated poor spatial alignment between road sections of high motorist safety risk and those of high value for wildlife connectivity. Transportation planning would benefit from integrating motorist safety risk and wildlife management needs to prioritize mitigation neighborhoods along roadways.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".