A spatiotemporal analysis of ungulate–vehicle collision hotspots in response to road construction and realignment
Bibliographic record
Abstract
Although roads are central to human society, they have many negative environmental impacts and create risk for traveling motorists. Our aim was to evaluate the spatiotemporal evolution of ungulate–vehicle collision (UVC) hotspots in response to major road construction. We examined two different locations and scales in the province of Alberta, Canada: (1) a highway bypass adjacent to a large city with 4.5 km of wildlife mitigation measures (wildlife fencing and two underpasses) and (2) 55 km of rural highway that was converted from a two-lane to a four-lane divided highway. Using government police collision and carcass data (2000–2021), before-after and control-impact analyses were used to assess changes in UVC rates. Our approach is novel in that we tested the paired use of a clustering method known as kernel density estimation plus and a spatiotemporal stepwise modification of this method to monitor UVC hotspots. By monitoring UVCs over space and time, we could identify stable vs. ephemeral UVC hotspots, a fence-end effect, and a barrier effect due to traffic volume, and we could explore hotspot stability before and after construction. The wildlife mitigation measures along the highway bypass resulted in 86% fewer UVCs compared to an unmitigated highway. At a larger scale, however, net benefits were affected by road density. The construction of a four-lane divided highway with no wildlife mitigation measures and an increase in the posted speed limit resulted in a slight increase in UVCs and the reemergence of the majority of historical UVC hotspots. Our analysis highlighted the need to incorporate wildlife considerations at a variety of scales throughout the transportation planning and mitigation evaluation process.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".