Progress in Heat Watch–Warning System Technology
Bibliographic record
Abstract
Among all atmospheric hazards, heat is the most deadly. With such recent notable heat events as the Chicago Heat Wave of 1995, much effort has gone into redeveloping both the methods by which it is determined whether a day will be “oppressive,” as well as the mitigation plans that are implemented when an oppressive day is forecast to occur. This article describes the techniques that have been implemented in the development of new synoptic-based heat watch–warning systems. These systems are presently running for over two dozen locations worldwide, including Chicago, Illinois; Toronto, Ontario, Canada; Rome, Italy; and Shanghai, China; with plans for continued expansion. Compared to traditional systems based on arbitrary thresholds of one or two meteorological variables, these new systems account for the local human response by focusing upon the identification of the weather conditions most strongly associated with historical increases in mortality. These systems must be constructed based on the premise that weather conditions associated with increased mortality show considerable variability on a spatial scale. In locales with consistently hot summers, weather/mortality relationships are weaker, and it is only the few hottest days each year that are associated with a response. In more temperate climates, relationships are stronger, and a greater percentage of days can be associated with an increase in mortality. Considering the ease of data transfer via the World-Wide Web, the development of these systems includes Internet file transfers and Web page creation as components. Forecasts of mortality and recommendations to call excessive-heat warnings are available to local meteorological forecasters, local health officials, and other civic authorities, who ultimately determine when warnings are called and when intervention plans are instituted.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".