Improving attribution of extreme heat deaths through interagency cooperation
Bibliographic record
Abstract
Attributing individual deaths to extreme heat events (EHE) in Canada and elsewhere is important for understanding the risk factors, protective interventions, and burden of mortality associated with climate change. However, there is currently no single mechanism for identifying individual deaths due to EHE and different agencies have taken different approaches, including (1) vital statistics coding based on medical certificates of death, (2) probabilistic methods, and (3) enhanced surveillance. The 2018 EHE in Montréal provides an excellent case study to compare EHE deaths identified by these different approaches. There were 353 deaths recorded in the vital statistics data over an 8-day period, of which 102 were potentially attributed to the EHE by at least one approach and 251 were not attributed by any approach. Only nine of the 102 deaths were attributed to the EHE by all three approaches, 23 were attributed by two approaches, and 70 were attributed by only one approach. Given that there were approximately 50 excess deaths during the EHE, it remains unclear exactly which of the total 353 deaths should be attributed to the extreme temperatures. These results highlight the need for a more systematic and cooperative approach to EHE mortality in Canada, which will continue to increase as the climate changes.
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 | 0.059 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".