Methodology for Prioritizing Appropriate Mitigation Actions to Reduce Wildlife-Vehicle Collisions on Idaho Highways
Bibliographic record
Abstract
Vehicle collisions with large wild animals are a safety issue for motorists and an ecological concern for wildlife populations. The objective of this research was to advance the efficacy of Idaho Transportation Department’s (ITD’s) project planning to reduce vehicle collisions with wildlife and to provide wildlife connectivity options across and under roads. A Wildlife-Vehicle Collision (WVC) Prioritization Process was developed through lessons learned from other U.S. States and Ontario Canada’s efforts, and Geographic Information System (GIS) modeling of data and maps already available in Idaho. The GIS maps were based on WVC crash and carcass data, Wildlife Highway Linkages maps, and species’ habitat maps. The resulting maps of WVC priority areas statewide and within ITD districts were the first of a 13 step process developed for the project. Users of this process further identify priority areas in ITD Districts based on other data such as: Idaho Fish and Game (IDFG) knowledge of wildlife populations, transportation plans, land ownership, field surveys of existing structures, options such as fencing, bridges, and culvert, and their cost-effectiveness. This WVC Prioritization Process was a step along a series of actions which ITD has undertaken and will continue to take to reduce risks associated with WVC and provide wildlife connectivity along Idaho roads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.001 |
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 teacher head, 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".