Can a healthy forest form part of the cure at one of Canada's busiest hospitals? A report on the healing opportunity of the trees and woodlots of Sunnybrook Health Sciences Centre, Bayview Campus
Bibliographic record
Abstract
During World War II the Government of Canada sought land for a hospital and asked the City of Toronto for a site. Toronto chose Sunnybrook Park, and extolled the health benefits of its trees, woodlots, creeks, fields and wildlife. The hospital opened in 1948. In Sunnybrook’s first decades, patients and staff enjoyed its natural beauty. Hard surface covered 12% of the hospital’s 42 hectares. By 2018, Sunnybrook’s greenspace has diminished. Sunnybrook today offers parking for over 4,500 cars. Today hard surface covers 52% of the hospital land. University of Toronto interns in 2018 assessed Sunnybrook street trees using the Neighbourwoods protocol. There is evidence of tree stress: Increase in hard surface under Sunnybrook street trees has a statistically significant impact on crown defoliation. Recommendations include (1) new transportation solutions to avoid need for more parking lots, (2) situating new buildings on existing hard surface to avoid tree loss, (3) improved tree planting methods, and (4) reconnecting Sunnybrook patients and staff to the hospital’s ample remaining trees, greenspace and woodlots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".