Reflections on a Century of Extreme Heat Event‐Related Mortality Reporting in Canada
Bibliographic record
Abstract
Climate change is causing more frequent and severe extreme heat events (EHEs) in Canada, resulting in significant loss of life. However, patterns across mortality reporting for historical EHEs have not been analyzed. To address this gap, we studied deaths in Canadian EHEs from 1936 to 2021, identifying trends and challenges. Our analysis revealed inconsistencies in mortality data, discrepancies between vulnerable populations identified, difficulties in determining the cause of death, and inconsistent reporting on social vulnerability indicators. We provide some observations that could help inform solutions to address the gaps and challenges, by moving toward more consistent and comprehensive reporting to ensure no population is overlooked. Accurately accounting for affected populations could help better target evidence-based interventions, and reduce vulnerability to extreme heat.
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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 | 0.038 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".