Bibliographic record
Abstract
for the City of Toronto Toronto has been at the forefront of organized green roof activity over the last several years. In early 1990’s volunteers under the Rooftop Garden Resource Group (RGRG) started to promote green roof development in the city. This has been taken over by Toronto-based Green Roofs for Healthy Cities, a not for profit organization, wh ich carries out world-wide education on green roofs. The City of Toronto has been an active participant in studying wider use of green roofs as a sustainable alternative to meet many of the urban environmental challenges. In the past few years the City has shown leadership in promoting green roofs. In order to inform its actions, the City in partnership with O C E-ETech and the Federation of Canadian Municipalities, engaged a team of Ryerson researchers to develop further understanding of: the types of available green roof technology, the measurable benefits of green roofs to the city’s environment, potential monetary savings to the municipality through use of green roofs, and minimum thresholds of green roofs that could be used for part of any incentives or programs. This report presents the findings on the municipal level benefits of implementing green roof
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.527 | 0.406 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".