Nature-Based Solutions (NBSs) to Mitigate Urban Heat Island (UHI) Effects in Canadian Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canada is warming at double the rate of the global average caused in part to a fast-growing population and large land transformations, where urban surfaces contribute significantly to the urban heat island (UHI) phenomenon. The federal government released the strengthened climate plan in 2020, which emphasizes using nature-based solutions (NBSs) to combat the effects of UHI phenomenon. Here, the effects of two NBSs techniques are reviewed and analysed: increasing surface greenery/vegetation (ISG) and increasing surface reflectivity (ISR). Policymakers have the challenge of selecting appropriate NBSs to meet a wide range of objectives within the urban environment and Canadian-specific knowledge of how NBSs can perform at various scales is lacking. As such, this state-of-the-art review intends to provide a snapshot of the current understanding of the benefits and risks associated with the implantation of NBSs in urban spaces as well as a review of the current techniques used to model, and evaluate the potential effectiveness of UHI under evolving climate conditions. Thus, if NBSs are to be adopted to mitigate UHI effects and extreme summertime temperatures in Canadian municipalities, an integrated, comprehensive analysis of their contributions is needed. As such, developing methods to quantify and evaluate NBSs’ performance and tools for the effective implementation of NBSs are required.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it