Progress in Heat Watch–Warning System Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it