The Effects of Changing Weather on Public Health
Bibliographic record
Abstract
Many diseases are influenced by weather conditions or display strong seasonality, suggestive of a possible climatic contribution. Projections of future climate change have, therefore, compelled health scientists to re-examine weather/disease relationships. There are three projected physical consequences of climate change: temperature rise, sea level rise, and extremes in the hydrologic cycle. This century, the Earth has warmed by about 0.5 degrees centigrade, and the mid-range estimates of future temperature change and sea level rise are 2.0 degrees centigrade and 49 centimeters, respectively, by the year 2100. Extreme weather variability associated with climate change may especially add an important new stress to developing nations that are already vulnerable as a result of environmental degradation, resource depletion, overpopulation, or location (e.g. low-lying coastal deltas). The regional impacts of climate change will vary widely depending on existing population vulnerability. Health outcomes of climate change can be grouped into those of: (a) direct physical consequences, e.g. heat mortality or drowning; (b) physical/chemical sequelae, e.g. atmospheric transport and formation of air pollutants; (c) physical/biological consequences, e.g. response of vector- and waterborne diseases, and food production; and (d) sociodemographic impacts, e.g. climate or environmentally induced migration or population dislocation. Better understanding of the linkages between climate variability as a determinant of disease will be important, among other key factors, in constructing predictive models to guide public health prevention.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".