Characterizing non-E. coli coliforms as indicators of groundwater susceptibility via “big data”, geostatistical analysis, and machine learning
Bibliographic record
Abstract
Individuals reliant on private wells for drinking water face a significantly higher risk of acute gastrointestinal illness (AGI) compared to those serviced by public water systems, with approximately 1 in 10 Ontarians (~1.6 million people) belonging to this subgroup. Historically, groundwater microbial contamination studies have focused on E. coli presence/absence, with the roles of non-E. coli coliforms (NEC) and microbial magnitudes (CFU/100 mL) in assessing groundwater susceptibility consequently remaining understudied. This PhD thesis seeks to address these gaps via analyses of the Ontario Microbial Water Quality Dataset (OMWQD), which contains >1 million private well samples collected from ~300,000 wells between 2010 and 2021. Three distinct thesis objectives were designed and fulfilled, each analyzing a suite of groundwater quality parameters (i.e., NEC concentration, E. coli concentration, and the NEC:E. coli concentration ratio): 1) develop and evaluate provincial groundwater Contamination Indices which reflect 12 years of groundwater quality data; 2) characterize south Ontario contamination cycles and identify immediate and/or sustained effects of three extreme weather events on microbial contamination; and 3) develop a series of parameter-specific models to explain long-term groundwater contamination across Ontario. Contamination Indices were spatially compared to enteric infection rates and well density, with findings confirming that indices are not biased by rural population density and, based on statistically significant associations with infection rates, represent appropriate spatiotemporal reflections of long-term groundwater quality. Contamination cycle analyses identified E. coli concentration and the NEC:E. coli ratio as complementary metrics, with concurrent interpretation of their seasonal signals indicating that contamination via bypass mechanisms dominates winter months. Developed models suggest NEC may serve as appropriate indicators of the potential for generalized contamination, as the NEC model exhibited high goodness-of-fit on testing datapoints (91.9%). Overall, this work provides foundational evidence for the continued use of NEC as a groundwater quality indicator, improves our knowledge of spatiotemporal variations in contamination mechanisms across Ontario, and provides transferrable results due to the high number of private well samples, multi-year study period, and the study region’s diverse climate, geology, and topography.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".