VULNERABILITY OF WATERBORNE DISEASES TO CLIMATE CHANGE IN CANADA: A REVIEW
Bibliographic record
Abstract
This project addresses two important issues relevant to the health of Canadians: the risk of waterborne illness and the health impacts of global climate change. The Canadian health burden from waterborne illness is unknown, although it presumably accounts for a significant proportion of enteric illness. Recently, large outbreaks with severe consequences produced by E. coli O157:H7 and Cryptosporidium have alarmed Canadians and brought demands for political action. A concurrent need to understand the health impacts of global climate changes and to develop strategies to prevent or prepare for these has also been recognized. There is mounting evidence that weather is often a factor in triggering waterborne disease outbreaks. A recent study of precipitation and waterborne illness in the United States found that more than half the waterborne disease outbreaks in the United States during the last half century followed a period of extreme rainfall. Projections of international global climate change scenarios suggest that, under conditions of global warming most of Canada may expect longer summers, milder winters, increased summer drought, and more extreme precipitation. Excess precipitation, floods, high temperatures, and drought could affect the risk of waterborne illness in Canada. The existing scientific information regarding most weather-related adverse health impacts and on the impacts of global climate change on health in Canada is insufficient for informed decision making. The results of this project address this need through the investigation of the complex systemic interrelationships between disease incidence, weather parameters, and water quality and quantity, and by projecting the potential impact of global climate change on those relationships.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".