A Stitch in Time: Improving Public Health Early Warning Systems for Extreme Weather Events
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
Extreme weather events, particularly floods and heat waves, annually affect millions of people and cause billions of dollars of damage. In 2003, in Europe, Canada, and the United States, floods and storms caused 15 deaths and US$2.97 billion in total damages, and the extended heat wave in Europe caused more than 20,000 excess deaths (1); the impacts in developing countries were substantially larger. There is a growing body of scientific research suggesting that the frequency and intensity of extreme weather events are likely to increase over the coming decades as a consequence of global climate change (2). These events cannot be prevented, but their consequences can be reduced by taking advantage of advances in meteorologic forecasting in the development and implementation of early warning systems that target vulnerable regions and populations. The skill with which weather and climatic events can be forecast has increased significantly over the past 30 years as more has been learned about the climate system. During this period, weather forecasting improved from the same-day forecast to the advance forecast. Our understanding of the mechanics and teleconnections of El Nino/Southern Oscillation now provides us with the capacity for seasonal and annual forecasting—assumed as recently as the 1970s to be more science fiction than fact (3). In fact, Chen et al. (4) recently suggested that El Nino events can be predicted 2 years in advance. Public health professionals have the opportunity to integrate weather- and climate-related information into local and regional risk management plans to reduce the detrimental health effects of hazards as diverse as tropical cyclones, floods, heat waves, wildfires, and droughts (5, 6).
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 | 0.033 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it