Urban Wildlife in Toronto: Species, Threats and Attitudes Towards Human-Wildlife Coexistence
Bibliographic record
Abstract
Urbanization has resulted in ever greater human-wildlife interaction, which can lead to human-wildlife conflict. Hotline data from the Toronto Wildlife Centre between 2001 and 2013 was analyzed to understand relationships between the public and wildlife in the Greater Toronto Area. Results indicate that the public are largely concerned with sick, injured and orphaned animals as well as nuisance situations, and animals are admitted mostly due to being orphaned, experiencing bleeding or injury and due to hitting windows. Most species in the dataset can be categorized as urban exploiters. Comparing types of calls, species, threats, location and extracting attitudes towards wildlife, main results show that raccoons are largely disliked in Toronto and perching birds are liked. Perching birds, however, experience the most anthropogenic consequences in downtown Toronto, as shown by admittances resulting from window strikes. To promote human-wildlife coexistence, recommendations include: educating the public about wildlife and wildlife situations, including wildlife in management decisions, increasing green spaces and preserving natural habitats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".