A RAPID, DATA-DRIVEN APPROACH TO SETTING TEMPERATURE THRESHOLDS FOR HEAT HEALTH EMERGENCIES
Bibliographic record
Abstract
Background and Aims: During the summer of 2009 a hot weather event in Vancouver, Canada resulted in considerable excess mortality (Kosatsky et al. Submitted.). As a direct result local authorities decided to develop a Heat Health Emergency (HHE) action plan. In late spring 2010 the BCCDC was asked to provide a rapid, evidence-based recommendation for the temperature at which an HHE should be called. Methods: Data on all-cause mortality for the summers of 2004-2009 were obtained from the BC Vital Statistics Agency. Measured and forecasted temperatures at the Vancouver (coastal) and Abbotsford (inland) airports were obtained from Environment Canada for the same period. Candidate thresholds were identified and tested by combining forecasted and measured temperatures in four early warning scenarios with different lead times, which has been identified as a priority for such work (Hajat et al. 2010). Early warning estimates were compared with measured temperatures using their sensitivity and positive predictive values (PPV). Results: In Vancouver the relationship between early warning estimates and measured temperatures deviated considerably from the 1:1 line, which resulted in multiple false positive HHE identifications for a 2-day average of maximum temperatures •30ºC. All three true positive events were succesfully identified at the 18-and 6-hour lead times (sensitivity = 1, PPV = 0.43). In Abbotsford the relationship was more linear for a 2-day average of maximum temperatures •36ºC, but the three true positives were not all identified until the 6-hour lead time (sensitivity = 1, PPV = 0.6). Conclusions: The BCCDC recommended that the HHE early warning system be based on data from Abbotsford, where the relationship between predicted and measured temperatures was most stable. We also suggested that a long-term educational campaign should be concurrently developed, especially if the HHE system is based on 2-day averages (i.e. one hot day must pass to trigger it).
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".