Projected risk and vulnerability to heat waves for Montreal, Quebec, using Gaussian processes
Bibliographic record
Abstract
Urban areas face increasing climate risks and are at the forefront of adaptation challenges. Despite the growing number of cities that are developing adaptation plans, they often fail to implement, monitor, and evaluate them. This article addresses this issue by modelling a comprehensive risk assessment that includes vulnerability using Gaussian processes. Mortality during heat waves for the City of Montreal, Quebec, is used as a case study. The vulnerability model includes sensitivity components (age and socioeconomic variables) and an adaptive capacity component (a suitable level of vegetation to decrease the urban heat island effect). Various aging and climate scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) are used for projections up to year 2100. SHAP values are used to show features contributions to the model. As the climate warms, Montreal will face increasing summer mortality. The city should therefore increase its vegetation cover in vulnerable neighbourhoods. Despite inherent limitations to the complexity of risk modelling, this approach facilitates the implementation of adaptation solutions and their monitoring. Greater effort should be made in the future to improve comprehensive risk modelling and more research is required to validate which framework is best in closing the gap between science and political decisions. • Mortality during heat waves in Montreal is used as a case study. • Projections for various aging and climate scenarios are included. • Gaussian processes are used to model risk and vulnerability. • SHAP values are used to show features contributions to the model. • This modelling framework fills various gaps in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".