Utility of Expert‐Based Knowledge for Predicting Wildlife‐Vehicle Collisions
Bibliographic record
Abstract
ABSTRACT Wildlife‐vehicle collisions have important ecological, economic, and social effects. In North America and across northern Europe, moose ( Alces alces ) are one of the largest ungulates hit by motor vehicles. The force and increasing frequency of these collisions has resulted in a commitment by wildlife and transportation agencies to limit or reduce causal factors. In an effort to improve these mitigation strategies, we used the most readily available source of knowledge of collision factors, expert opinion, to develop a series of models that explained and predicted location of moose‐vehicle collisions (MVC). We developed expert‐based models using the Analytical Hierarchy Process (AHP) and we used a structured survey approach where experts could assess criteria relevancy, weight criteria, and review weights for consistency. We hypothesized that collisions were the product of habitat‐ or driver‐related factors and we formulated the survey accordingly. We used the receiver operating characteristic to validate the resulting models and the Kappa index of agreement to quantify differences among spatial predictions originating from the experts. Local and nonlocal experts weighted the moose habitat classification as the most important criterion for identifying MVC. Among driver‐related criteria, speed limit was weighted as the most important factor. Overall, habitat‐based models were more proficient than driver‐based models in predicting MVC within Mount Revelstoke and Glacier National Parks, Canada. Both local and nonlocal expert models were excellent predictors of MVC, with local experts slightly outperforming nonlocal experts. Considering that habitat‐related criteria were more powerful for predicting MVC, and that habitat can vary considerably across study areas, we suggest that local experts be used when possible. The AHP is a valuable tool for wildlife, highway, and park managers to better understand why and where wildlife‐vehicle collisions occur. Adopting this process, our data suggested that MVC were most strongly correlated with highway attractants associated with habitat. Vegetation management or alternative routing could minimize spatial juxtaposition of moose and motor vehicles.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".