Using strategic partnerships to advance urban heat island adaptation in the greater Toronto area
Bibliographic record
Abstract
Since 2006, Natural Resources Canada (NRCan) and the Clean Air Partnership (CAP) have been engaged in a capacity building collaboration focused on adapting to extreme heat in the Greater Toronto Area (GTA). Initial discussions focused on facilitating scientific data collection to identify and characterize urban heat vulnerabilities in the GTA, but evolved considerably in scope and breadth of focus over a five year period. The collaborative process led to the development of new climate change adaptation planning tools, facilitated the engagement of GTA municipalities in the use of geomatic information in planning to reduce the impacts of urban heat and motivated GTA municipalities to develop projects and programs to protect their communities from urban heat island effects and the expected impacts of climate change. The development of a strategic advisory group was critical in fostering the collaborative process and encouraged the creation of lasting partnerships and capacity building in the region. The group helped establish project goals and objectives, and provided a communications hub that spurred constructive dialogue, encouraging the creation of a range of new ideas and resultant projects. The advisory group demonstrated elasticity, a willingness to work together, an ability to reach agreement and to communicate effectively; essential traits in successful collaborations.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".