A short note on the use of daily climate data to calculate Humidex heat‐stress indices
Bibliographic record
Abstract
Abstract The projected increase in the frequency and intensity of extreme heat events due to climate change means an associated increase in risk of heat‐related illnesses and mortality. Public health systems need to be prepared to identify and reduce the susceptibility of vulnerable populations to increased occurrence of heat‐related illness and stress. To facilitate this, climate services have begun developing climate change projections for heat‐stress indices based on exceedances of thresholds used operationally in meteorological heat warning systems. This task is complicated by the fact that heat‐stress indices are generally computed using hourly data whereas climate model outputs are often archived at daily or longer time steps. This study focuses on Humidex, a heat‐stress index used in heat alerts issued by the Meteorological Service of Canada. Several potential solutions for computing robust Humidex indices using daily data are examined, including a new approximation method. Indices obtained with the new method are compared with indices obtained using the classic method based on hourly data as well as with other two methods based on average daily values. The new approximation gives good estimations for humidex indices, while the daily‐average‐value methods present biases with respect to the hourly‐value method.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".