Bibliographic record
Abstract
Trees are a critical asset that provides numerous benefits to the people and environment in urban settings. Toronto District School Board (TDSB), the largest school board in Canada, has taken various climate mitigation actions, where tree planting is one of the most important mitigation strategies. Trees on school properties significantly reduce climate change impacts and help reduce energy consumption with their shade. They also improve the overall well-being and academic performance of students. While TDSB currently has approximately 35,000 trees on its properties, tree planting programs constantly help to increase the number of trees and tree canopy. However, despite these efforts and concerns were raised by TDSB trustees and parent councils that tree planting was not equitable and fair in terms of socio-economic and geographic factors. This study focused on tree planting priorities based on schools' equity socio-economic and environmental characteristics. The priority planting analysis was conducted using TDSB spatial data and a data-driven method. The analysis outcomes were compared with the City of Toronto's heat vulnerability index for validation. Recommendations were provided regarding future tree planting practices at TDSB. The results of this study could be used to conduct future research regarding equitable distribution of trees and improve overall sustainability at TDSB.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".