Road Mortality of Reptiles and Other Wildlife at the Ojibway Prairie Complex and Greater Park Ecosystem in Southern Ontario
Bibliographic record
Abstract
The Ojibway Prairie Complex in Windsor contains the largest protected tallgrass prairie ecosystem in Ontario and supports numerous species at risk. Despite its ecological significance, it is crossed by multiple high-traffic roads. Road mortality is a major threat to endangered species in Canada, particularly reptiles. The main goal of this study was to describe the nature and extent of vertebrate road mortality, with a focus on reptiles, on roads bisecting the Ojibway Prairie Complex, and the Greater Park Ecosystem, in Windsor and Lasalle, Ontario. A systematic road mortality survey was conducted by bicycle along seven roads (12.5 km) in 2010, 2012, and 2013. Also, opportunistic observations (n = 103) spanning over 30 years were assembled from a variety of sources. In total, 2083 vertebrates (49 species), including 446 reptiles (11 species), were recorded “dead onroad” during systematic surveys. The highest diversity of reptiles was recorded on Matchette Road, whereas the highest rate of reptile mortality was recorded on Malden Road. Reptile species at risk were killed on all roads surveyed. Combining systematic and opportunistic data, we found seven reptile species at risk: Butler’s Gartersnake (Thamnophis butleri), Eastern Foxsnake (Pantherophis vulpinus), Eastern Massasauga (Sistrurus catenatus catenatus), Blanding’s Turtle (Emydoidea blandingii),Eastern Musk Turtle (Sternotherus odoratus), Northern Map Turtle (Graptemys geographica), and Snapping Turtle (Chelydra serpentina). Reptile road mortality “hotspots” occurred where each road is intersected by a naturalized utility right-of-way. Our results can be used to focus mitigation efforts in space and time to reduce mortality rates and enhance connectivity in the Ojibway Prairie Complex and Greater Park Ecosystem.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".