Are Urban Fruit Trees Healthy? Examining Health Indicators in Toronto’s Urban Orchard
Bibliographic record
Abstract
While urban fruit trees can provide food security and community, they are an understudied part of urban forestry. Using a Neighbourwoods© inventory (Kenney and Puric-Mladenovic, 1995), the purpose of this report was to examine how health varied across species, size classes, and neighbourhoods. Homeowners were requested to register their trees in the survey and 162 fruit trees were sampled in total. Each sample involved a Neighbourwoods© health assessment and a short interview with the owner where they were asked about the tree’s health and history. There was no significant difference in health rating between species, though apricots were the most likely to be extremely unhealthy. However, there was a significant difference in defoliation levels between species, with apricots being the most heavily defoliated. Additional findings from interviews suggest that homeowners are reluctant to care for their fruit trees due to a lack of knowledge, a perceived lack of time or energy, or the desire to remove them at some point. The most common reasons for removal were the mess of falling fruit or a new owner moving in after the tree was planted. Variance in health is important to examine in urban fruit trees to better understand which tree species might be the least healthy, the least productive, and the most likely to be replaced or removed soon. Interviewing homeowners is important to better understand where their frustrations and knowledge gaps lie, so urban forestry organizations and municipalities can tailor their services and educational programs.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".