The Locations and Drivers of Herpetofaunal Wildlife Road Mortality on Two Highways within the Frontenac Arch, Ontario
Bibliographic record
Abstract
The mortality of wildlife on roads is an aspect of biological and environmental degradation that is often overlooked amidst the plethora of other threats that exist. Yet it is a very real concern, especially for species that are more susceptible to death on roadways, such as the herpetofauna. In this research, I utilized databases of herpetofauna road mortality from two highways in the Frontenac Arch region in eastern Ontario. Data from regular surveys I conducted on a 38km section of Ontario’s Highway 2 (2016 and 2017), as well as previous surveys conducted on the 37km Thousand Islands Parkway (2008 and 2010) were used to explore where and why mortality is occurring in this area. Kernel density analysis for the taxonomic groupings included in this research showed that road mortality was not random along the roads and there was spatial clustering in the form of hotspots. The hotspots of every taxonomic group overlapped in the middle of Highway 2, while hotspots on the Thousand Islands Parkway were more variable. There is an expanse of forest and wetland that intersects with the hotspot areas of Highway 2, and there was some activity on the Parkway where the road intersects this forest and wetland, but mortality is not as significantly clustered as on Highway 2. Regression tree analyses showed that, across roadways, wetland and water-related variables are important factors influencing the location of frog and toad, turtle, and watersnake mortality. An overarching trend from the results of the regression tree analyses was that mortality was generally higher in areas with lower traffic levels and in areas lacking development such as roads and urbanization. This suggests that populations in these areas may be avoiding roads, or are depressed due to prolonged exposure to roads and high traffic.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".