Water resources in a changing climate: Gastrointestinal illness as a sentinel for water quality
Bibliographic record
Abstract
Background Diarrheal disease causes significant mortality and morbidity around the world. Extreme weather events increase the risk of diarrheal illness by contaminating water sources during high rainfall run-off and flood events or during droughts. These extreme events are increasing in frequency and intensity, a trend that is expected to worsen. British Columbia (BC), is a large Canadian province with very diverse hydrological regimes and therefore a useful place to study vulnerability to water stress. The study communities were selected to represent different hydro-climatic regimes and residential drinking water sources. Aim To examine the role of season, water source and pathogen type in sporadic gastro-intestinal (GI) illness in different hydro-climatic regimes over an eleven year period 1999-2009. Methods and Results 2,308 cases of laboratory confirmed reported campylobacter, verotoxic E. coli, salmonella, giardia and cryptosporidium were analyzed. Three methods were used to characterize the seasonality of GI illness; time-series plots, monthly plots and spectral analysis. Using three methods allowed for triangulation of results and a more in depth understanding of seasonality. Each of these three methods was applied to all 8 communities aggregated in a time-series, as well as the time-series disaggregated by hydro-climatic regime, drinking water source, and pathogen type. Our results indicate that mixed systems are more hazardous than surface water or groundwater alone, cases occurring in snow melt-dominated watersheds peak in July vs. September for rain dominated regimes, and parasitic infection peaks about 6 weeks after bacterial. We are also exploring environmental risk factors for GI illness including temperature, rainfall extremes and high flows in source river systems. Conclusions Local research using GI illness data can highlight seasonal patterns, risk factors and identify vulnerabilities for municipal water systems. Such tools will be critical for planning adaptive measures for potable water in a changing climate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".