Mapping Drinking Water Systems for Population Exposure Assessment
Bibliographic record
Abstract
Drinking water is a recognized pathway through which populations can be exposed to biological and chemical hazards. We mapped Ontario Municipal Drinking Water Distribution systems (DWDS) by matching census areas to images of drinking water pipe networks. The new maps enable ongoing epidemiological research and surveillance by allowing linkage of both drinking water monitoring data and administrative health data sets to the residence of the population served.The location of drinking water distribution pipes for systems serving a population of 5,000, or more, were obtained from the Ontario Ministry of Environment and Climate Change (MOECC). Images of pipe networks varied widely in resolution and detail, and were scaled and aligned by a GIS analyst using Google Earth and ArcMap v10.3. The smallest geographic areas for which Canadian census includes population, age and sex data i.e. “dissemination areas” (DAs) were used to approximate the boundaries of water systems.The selected DAs were spatially joined to form 153 polygons which include area home to 10,930,166 people (85% of the Ontario population). Each DWDS polygon has a unique MOECC identification number to enable patient address and health-data linkage to extensive water quality and treatment data. Population data from DAs were pooled within a DWDS to enable water-system level calculations of disease incidence and prevalence rates, standardized for age and sex.As census boundaries do not match those of DWDS, the maps created unavoidably include some error. This error can be quantified as the proportion of the Ontario population currently receiving drinking water from private systems erroneously assigned to a DWDS due to proximity. Methods to quantify this error are discussed, including the registry of private domestic wells which reveals error increasing as system size decreases. Analysis reveals that the error proportion can generally be limited to less than10% for population areas of less than 13,000 people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".