Climate change and infectious diseases: What can we expect?
Bibliographic record
Abstract
Global climate change, driven by anthropogenic greenhouse gas emissions, is being particularly felt in Canada, with warming generally greater than in the rest of the world. Continued warming will be accompanied by changes in precipitation, which will vary across the country and seasons, and by increasing climate variability and extreme weather events. Climate change will likely drive the emergence of infectious diseases in Canada by northward spread from the United States and introduction from elsewhere in the world via air and sea transport. Diseases endemic to Canada are also likely to re-emerge. This special issue describes key infectious disease risks associated with climate change. These include emergence of tick-borne diseases in addition to Lyme disease, the possible introduction of exotic mosquito-borne diseases such as malaria and dengue, more epidemics of Canada-endemic vector-borne diseases such as West Nile virus, and increased incidence of foodborne illnesses. Risk is likely to be compounded by an aging population affected by chronic diseases, which results in greater sensitivity to infectious diseases. Identifying emerging disease risks is essential to assess our vulnerability, and a starting point to identify where public health effort is required to reduce the vulnerability and exposure of the Canadian population.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".