An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using “big data”, time-series analyses, and unsupervised machine learning
Bibliographic record
Abstract
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non- E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, “big data” (i.e., large, diverse datasets that grow over time) have yet to be employed for assessing the effects of high return-period extreme weather events on groundwater quality. The current investigation employed ≈1.1 million Ontarian private well samples collected between 2010 and 2021, seeking to address these knowledge gaps via applying time-series decomposition, interrupted time-series analysis (ITSA), and unsupervised machine learning to five microbial contamination parameters: E. coli and NEC concentrations (CFU/100 mL) and detection rates (%), and the calculated NEC: E. coli ratio. Time-series decompositions revealed E. coli concentrations and the NEC :E. coli ratio as complementary metrics, with concurrent interpretation of their seasonal signals indicating that localized contamination mechanisms dominate during winter months. ITSA findings highlighted the importance of hydrogeological time lags: for example, a significant E. coli detection rate increase (2.4% vs 1.8%, p = 0.02) was identified 12 weeks after the May 2017 flood event. Unsupervised machine learning spatially classified annual contamination cycles across Ontarian subregions (n = 27), with the highest inter-cluster variability identified among E. coli detection rates and the lowest among NEC detection rates and the NEC: E. coli ratio. Given the spatiotemporal consistency identified for NEC and the NEC: E. coli ratio, associated interpretations and recommendations are likely transferable across large, heterogeneous regions. The presented study may serve as a methodological blueprint for future temporal investigations employing “big” groundwater quality data. • First “big data” (1,075,057 samples) temporal analysis of private groundwater quality. • Time-series decomposition reveals three distinct seasonal signal “categories”. • Seasonal signal clusters may provide a framework for future groundwater research. • NEC: E. coli ratios indicate localized contamination mechanisms dominate in winter. • Multiple hydrogeological assessment “cycles” may be necessary due to contamination time lags.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".