Differences in heat-related mortality across four ecological regions with diverse urban, rural, and remote populations in British Columbia, Canada
Bibliographic record
Abstract
Temperature-mortality analyses are challenging in rural and remote communities with small populations, but this information is needed for climate change and emergency planning. The geographic health areas of British Columbia, Canada were aggregated into four ecoregions delineated by microclimatic conditions. Time series models were used to estimate the effect of maximum apparent temperature on daily non-traumatic mortality. The population of the coldest ecoregion was most sensitive to hot weather, while the population of the hottest ecoregion was least sensitive. The effects were consistently strongest in decedents aged less than 75 years. A province-wide total of 815 deaths was attributed to hot weather over the 25-year study period, with 735 deaths in the most populous ecoregion. The framework described could be adapted to other climatically variable regions with urban, rural, and remote populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".