Spatial distribution of wildlife road mortality: How important is rigorous data collection?
Bibliographic record
Abstract
• We identified hotspots and coldspots at four scales for three confidence levels. • We compared rigorously collected data to data collected by highway patrol personnel. • We found considerably more animals and identified more species than highway patrol. • Rigorous surveys provide a better understanding of impacts of roads on biodiversity. • Installation of fences combined with designated wildlife passages is needed. We conducted 66 road mortality surveys along a 31.5 km stretch of a high-traffic 4-lane highway between Montreal and Sherbrooke in Québec, Canada. Surveys by vehicle between May and August 2019 recorded 212 animal carcasses from 48 species. Hotspots and coldspots for ground-dwelling vertebrates (mammals, amphibians, and reptiles) and birds were identified at four scales. The number of hotspots was higher at finer scales, while the combined length of hotspots was greater at coarser scales. We found significantly more animals and identified more species than the highway patrol, suggesting that rigorous road mortality surveys are beneficial for better understanding road impacts on biodiversity. We estimated that the highway patrol’s reporting probability for medium-sized mammals (more than 0.65 kg, less than 30 kg) was between 21 % and 54 % that of our systematic surveys. We recommend priority locations for mitigation to reduce road mortality and re-establish connectivity between wildlife populations separated by the highway.
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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 | 0.084 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| 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".