Are roadkill hotspots the best places to mitigate for road mortality?
Bibliographic record
Abstract
The common approach for mitigating the effects of road mortality is to install fencing at roadkill hotspots, where the most animals are being hit by vehicles.However, this approach could be missing populations that were previously depleted by heavy roadkill.Instead, mitigation could be targeted to "habitat-traffic hotspots," where high-quality habitat is intersected by a hightraffic road and where per capita road mortality is highest.We compared mitigation sites for amphibians, mammals, and reptiles using roadkill hotspots and habitat-traffic hotspots along 77.6-km of roads near Ottawa, Canada.We found that roadkill hotspots and habitat-traffic hotspots did not generally coincide, especially for reptiles and mammals.This means that roadkill hotspots may not be the best method for identifying mitigation sites if the objective is to install fencing where they can most effectively mitigate the population-level effects of roadkill. List of AppendicesAppendix A. Road mortality survey data table.Road surveys were completed along road segments once per week for a total of 15 weeks from May 10 th to August 19 th , 2021.Surveys were completed on weekdays between 7:00-18:00.Road segments were grouped into 3 geographic areas (A, B, and C) and we varied the order that areas and segments within areas were surveyed each week.Survey forms were completed at the start of each road mortality survey to record the date of the survey, start time, the area, the road segment name (1 of 39), the air temperature at the start of the survey (Celcius), the cloud cover in % (rounded to the nearest 10%), the wind level (Beaufort scale), precipitation (none, light, medium, heavy, snow), and whether it had rained in the last 24 hours.Each survey has a unique survey-ID which can be related to individual roadkill observations...............
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".