Climate change and infectious diseases: What can we expect?
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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