Report on the Environmental Benefits and Costs of Green Roof Technology for the City of Toronto
Bibliographic record
Abstract
This report presents the findings on the municipal level benefits of implementing green roof technology in the City of Toronto. Beyond this report, which addresses the immediate needs of the City of Toronto in assisting them to formulate appropriate programs and policies, the \nRyerson team has been charged by OCE-ETech to develop a generic technological solution that can be used to predict the costs and benefits related to green roofs. This work is ongoing and is not reported here. Of the many benefits of green roofs reported in the study, the ones that had the most quantifiable monetary value based on currently available research data are: benefit from stormwater flow reduction including impact on combined sewer overflow (CSO), improvement in air quality, reduction in direct energy use, and reduction in urban heat island effect. The literature review indicated other benefits that could not be quantified in this report. These benefits included: aesthetic improvement of urban landscape, increase in property values, benefits resulting from green roofs used as amenity spaces, use of green roof for food production, and increased biodiversity. Further work is needed to quantify these benefits.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".