Spatial distribution of heat vulnerability in Toronto, Canada
Bibliographic record
Abstract
The frequency, intensity and duration of heatwaves are expected to increase in Toronto, Canada due to both climate change and the urban heat island effect. This poses a greater health risk to those who are most vulnerable to heat among a population of almost three million residents. Therefore, designing and implementing appropriate heat management measures requires information about how heat vulnerability is distributed across the city. To fill the knowledge gap, two distinct methods are examined in this study to quantitatively measure the spatial distribution of heat vulnerability in Toronto. Both heat vulnerability indices (HVIs) consist of three dimensions, exposure, sensitivity and adaptive capacity, that are aggregated from remotely sensed land surface temperature measurements and socio-economic census data. The first method uses principal component analysis to derive an HVI, while the second, simpler method assigns equal weight to each input variable to derive an HVI. The HVIs display a similar U-shaped pattern of high heat vulnerability across Toronto, with low heat vulnerability areas primarily located along the Lake Ontario shoreline and throughout the fluvial ravine system. Further cluster analysis reinforces this spatial pattern. Notably, this study highlights that low-income tower block communities are significantly more vulnerable to heat than the city average. The qualitative consistency between the two HVI methods allows for ease of adoption of the simpler, equal-weight method for future use by the city. Integration of HVI updates into municipal operations can allow city planners and managers to monitor and visualize heat vulnerability consistently over time, develop decision-support tools for heat emergency preparedness and response and assess the effectiveness of heat adaptation strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".