Detection and Attribution of Climate Change Effects on Infectious Diseases
Bibliographic record
Abstract
Infectious agents are likely to be sensitive to climate change if their life cycle includes periods of exposure to ambient conditions.Several studies have attempted to attribute changes in patterns of infectious diseases to recent climate change, such as resurgent malaria in the East African Highlands and the northward expansion of tick-borne encephalitis and Lyme disease in Europe and Canada.However, debate continues over the relative importance of climate change compared to social, demographic and other factors.Methods for the detection and attribution of climate change impacts on human infectious diseases have not been clearly defined.There are several areas of contention in the literature on appropriate methods for the detection of climate change effects on infectious diseases, including the availability and appropriate use of climate data, identifying regions where changes are most likely to be observed and the biological importance of small temperature increases and threshold effects.Definitions and strategies for the detection and attribution of climate change impacts on human infectious diseases are discussed and compared to approaches to the detection and attribution of climate change impacts in other fields.'Consistency analysis' is proposed as a feasible methodological approach to address research questions about the impact of recent climate change on infectious diseases.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".