Development of Technical Guidance to Advance Surface and Air Temperature Mapping and Heat-health Vulnerability Mapping in Canada
Bibliographic record
Abstract
Urban heat island (UHI) effects exacerbate extreme heat risks, particularly for vulnerable populations in urban areas, as climate change intensifies the frequency and severity of heat events, resulting in serious health impacts. Surface and air temperature maps, along with heat-health vulnerability maps, are crucial tools for understanding and mitigating heat-related risks. These enable stakeholders to identify the key drivers of heat exposure, evaluate community impacts, and prioritize targeted interventions. Currently, Canada lacks a pan-Canadian approach for developing standardized surface and air temperature and heat-health vulnerability maps, which are essential for comparing heat-health exposure and vulnerability across various communities. To fill this gap, researchers at Concordia University and Toronto and Region Conservation Authority (TRCA) are leading two complementary projects, supported by the Standards Council of Canada (SCC) and Health Canada (HC). These projects aim to develop technical guidance for advancing surface and air temperature maps and heat-health vulnerability maps in Canada, focusing on identifying and recommending best practices that are adaptable and accessible for communities across the country. The projects will involve a comprehensive analysis of existing mapping methodologies through a systematic literature review and active engagement with subject matter experts and map users through a national workshop and multi-disciplinary steering committees to gather feedback. The final report will deliver a detailed evaluation of mapping methodologies, covering available data sources, technical requirements, temporal and spatial scales, implementation complexity, target audiences, and use cases. For surface and air temperature mapping, the study will explore methods of remote sensing, numerical models, field observations, reanalysis data, and coupled method frameworks. For heat-health vulnerability maps, factors that influence people’s vulnerability and adaptive capacity to extreme heat will be evaluated. The project emphasizes a consensus-based approach to determine practical and achievable mapping methods that may lay the foundation for a potential National Standard of Canada.
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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 | 0.067 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".