Comparing Species Composition and Planting Trends: Exploring Pest Vulnerability in Toronto’s Urban Forest
Bibliographic record
Abstract
Urban forests represent a valuable resource for cities but are not without costs. These costs can include time, money, and the loss of beneficial services as results of pest infestations. Knowledge of an urban forest’s tree species composition and vulnerability to pests is needed to help managers enhance services delivered, while minimizing expenses over the long-term. Recent research has explored the impacts of individual pests on urban forests, but less attention has been given to the overall pest vulnerability. In this research, tree genera currently prevalent and commonly planted in Toronto, Ontario, Canada, were analyzed using a pest vulnerability matrix to explore how the city’s urban forest species composition and pest vulnerability may be changing. Current tree species composition was derived from existing inventory data, while the planting trends of a variety of local actors were determined through surveys and interviews. Results indicate there is somewhat limited diversity in current street and non-street tree populations, as well as a number of common tree species that have severe pest vulnerabilities. While new plantings replicate some current composition and pest vulnerability issues, several less common species are also being planted. As a result, overall pest vulnerability should decrease in the future, while some ongoing management concerns remain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".