Integrating sustainability subsystems in the management of urban forests
Bibliographic record
Abstract
The urban forest is an important natural capital asset providing essential ecological, social, and economic benefits to people living in cities. Research contained within this dissertation examines urban forest structure and management through the lens of strong sustainability and has as its central focus the question of where to prioritize planting of trees in a densely populated, and continually expanding, North American urban centre. Three independent research studies are included, each of which addresses a dimension of the urban forest that falls within one of the three subsystems of sustainability. The first study focuses on urban forest ecological service delivery with a specific focus on the relationship between forest canopy closure and summer surface temperatures across the City of Toronto, Canada. The second study examines a social dimension of the urban forest—identifying distributional inequalities in city resident access to urban tree canopy as a function of their household income. In the third study, an economic dimension of urban sustainability is investigated by examining the legacy of street tree planting decisions and their relationship to ash tree mortality caused by the emerald ash borer (Agrilus planipennis). In addition to adding to scholarship concerning the processes and relationships examined within each sustainability subsystem, common themes arising across each of the studies are identified and discussed. These individual research studies and intersecting themes serve as the basis for an innovative approach to prioritizing urban tree planting that seeks to integrate a sustainability subsystems approach to the decision-making process.
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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 | 0.002 | 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".