Green roofs : a resource manual for municipal policy makers
Bibliographic record
Abstract
As knowledge of the environmental benefits of green roofs and technology improves, green roofs are quickly gaining acceptance in North America. European jurisdictions have been using green roof technology for stormwater management, to reduce energy use in buildings and to increase amenity space. By reviewing the reasons that municipalities throughout the world have set green roof policies and programs, policy makers can more easily determine which policies suit their needs. This manual provided an overview of international and Canadian green roof policies and programs. It presented information on 12 jurisdictions that demonstrated leadership in green roof policy development. The manual also presented information on an additional 13 jurisdictions with less-developed green roof policies. Activities that were discussed for each of these jurisdictions included: description of jurisdiction; key motivators; green roof policy; process to establish policy; effectiveness; lessons learned; future predictions; and applicability to Canada of international jurisdictions. The manual also provided general information on green roofs such as a definition of green roofs and green roof terminology. Key motivators for green roofs include stormwater runoff control; reduction in urban heat-island effect; reduction in building energy consumption; and air pollution control. refs., tabs., figs.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.348 | 0.197 |
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".