Road Kill at the Leslie Street Spit: Assessing the Road Mortality Patterns in Toronto’s Urban Wilderness (abstract)
Bibliographic record
Abstract
The presence of roads is a necessary component of urban life, but the manner in which the urban road network affects the surrounding natural environment—and, more specifically, the urban wildlife—is not often considered. The danger posed to wildlife in urban environments by means of vehicle‐related fatalities is prevalent, even in areas of urban wilderness such as the Leslie Street Spit (the Spit) in Toronto, Ontario. The rate at which some species are killed on urban roads can be catastrophic to the population and possibly lead to extirpation. The goal of this study is to identify patterns of road mortality and, ultimately, to contribute data for effective mitigation strategies to reduce the instances of road kill at the Spit. The study area was divided into four sections and, from May to August 2011, the location and species of road kill found were recorded and mapped. The total count of road mortalities was 96, with snakes accounting for 71%, followed by birds (18%), amphibians (7%), and mammals (4%). The two sections of the study area with the highest vehicle traffic accounted for the majority (61%) of road mortalities. Temporal differences by taxonomic group were observed; for example, the mortality of snakes peaked in June, and that of birds in July and August. Overall, the results show that snakes are disproportionately affected by the presence of vehicles on the roads at Toronto’s Leslie Street Spit and that the areas of increased traffic are also areas of increased mortality for the wildlife in this urban wilderness area.
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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 | 0.000 |
| 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".