Exploring the Urban Forest: Evidence of Tree Condition Change across Toronto’s Environmental Gradient
Bibliographic record
Abstract
Increasing urbanization creates environmental impacts on flora, fauna, and human populations. Urban trees provide mitigating services that may be maximized by understanding environmental stressors that impact tree health. I explored the relationship between tree condition and urban landcover as evidence of urban stressors. Tree morphology, canopy condition, and insect abundance were expected to vary across an urban land cover gradient, at different spatial scales, and between native/exotic species. These responses were explored in Tilia americana, Tilia cordata, Acer platanoides and Acer saccharnium in Toronto, Ontario, Canada. Specific spatial scales and environmental variables better explained changes in tree response variables. My results suggest Tilia species were more tolerant of urban conditions. Differences between species were described by genera suggesting species trait similarity may determine environmental suitability. Maximizing service provision requires interspecific stress expression consideration. Further research will explore soil conditions, socio-economic factors, and environmental remote sensing in urban tree condition modelling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".