Cumulative and relativistic temperature metrics for public heat alerts: A new approach proposed for greater Vancouver, Canada
Bibliographic record
Abstract
Abstract Background Many heat alert systems rely on fixed absolute temperature thresholds that may not fully characterize risk. Furthermore, absolute temperatures can vary widely across some urban areas, leading to the same absolute thresholds being associated with different risks. The impending modernization of meteorological services in Canada provides an opportunity to address these challenges with heat alerts in greater Vancouver, British Columbia (BC). Objectives (1) To evaluate a cumulative heat alert indicator based on the sum of high temperatures over two consecutive days and the intervening overnight low (High + Low + High, or H+L+H) using mortality and heat-related morbidity data. (2) To compare the performance of absolute and relativistic H+L+H thresholds for generating heat alerts, including consideration of susceptible populations. Methods Time-series analyses were used to examine the relationships between all-cause mortality (2008-2024), heat-related emergency department (ED) visits (2014-2023), and H+L+H temperature observations from three weather stations across greater Vancouver. For each station, the outcomes were modelled using absolute and relativistic H+L+H thresholds, including analyses stratified by age, sex, socioeconomic status, and chronic health conditions. Results The relativistic H+L+H indicator was associated with significant risk of morbidity and mortality, with similar exposure-response functions for all three weather stations. In contrast, absolute thresholds showed considerable heterogeneity. The relativistic metric also showed different risk functions for some susceptible groups, including those with schizophrenia, Parkinson’s disease, and receiving income assistance. Conclusion The relativistic H+L+H temperature metric integrates cumulative multi-day exposure and addresses the challenge of intra-urban variability in absolute temperatures. This approach provides a more flexible alternative to heat alerts based on absolute temperatures, and highlights differences in risk among susceptible populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".