Évaluation et cartographie de la vulnérabilité à la chaleur dans l'agglomération de Montréal
Bibliographic record
Abstract
Cet article propose une méthodologie basée sur l'utilisation d'un système d'information géographique (SIG) pour évaluer la vulnérabilité des populations urbaines aux épisodes de chaleur extrême dans le contexte des changements climatiques. L'objectif est de produire une méthode d'évaluation simple et reproductible capable de croiser des facteurs sociaux et physiques afin d'identifier et de cartographier les secteurs les plus vulnérables en prenant comme étude de cas l'île de Montréal. Nous pouvons dégager trois éléments fondamentaux de la vulnérabilité thermique dans la littérature : l'exposition, la sensibilité et la capacité d'adaptation des populations. Les variables choisies pour évaluer la vulnérabilité thermique sont : 1) la présence d'îlots de chaleur urbains calculée à partir d'une image satellitaire pour représenter l'exposition; 2) la proportion de personnes âgées de 65 ans et plus vivant seules pour la sensibilité et 3) l'indice de défavorisation matérielle et sociale développé par Raymond et Pampalon pour exprimer la capacité d'adaptation. La cartographie résultante aide à hausser la résilience des municipalités en facilitant les interventions en cas de sinistre et en ouvrant la voie à l'implantation d'actions de prévention ciblées et efficaces. Cette étude constitue une avancée dans l'exploration des possibilités des SIG pour l'analyse environnementale. This article proposes a methodology based on the use of a geographic information system (GIS) to evaluate the vulnerability of urban populations to episodes of extreme heat in a context of climate change. The goal is to produce a simple and reproducible evaluation method, incorporating social and physical factors, to identify and map areas that are most at risk using the island of Montreal as a case study. We have identified three fundamental elements of thermal vulnerability from the literature: exposure, sensitivity and the populations' ability to adapt. The variables we selected to evaluate thermal vulnerability were: 1) the presence of urban heat islands calculated from satellite imaging to represent exposure; 2) the proportion of people aged 65 and over who live alone for the purposes of sensitivity; and 3) the material and social deprivation index established by Raymond and Pampalon to express adaptability. The resulting map helps to improve the resilience of municipalities by facilitating action in the event of an extreme heat incident and by creating a new means to implement targeted and efficient measures of prevention. This study is a major advance in the exploration of possibilities offered by GIS for environmental analysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".