Using “big data” and non-linear machine learning to infer groundwater contamination mechanisms across a spatially extensive, geologically heterogeneous region
Bibliographic record
Abstract
• First non-linear, big data contamination index model with associated partial effect plots • High goodness-of-fit (91.9%) non- E. coli coliform model, linked to groundwater recharge • Summer rainfall “tipping point” (3mm/day) may induce runoff and/or aquifer dilution • Deep wells and bedrock aquifers associated (p < 0.0001) with localized contamination • Microbial concentrations and NEC: E. coli ratio complement traditional quality metrics Groundwater accounts for approximately 98% of available freshwater, with >2 billion people relying on it as a primary drinking water source. Notwithstanding its importance, specific groundwater quality parameters - namely microbial concentrations and non- E. coli coliforms (NEC) - remain understudied. The current study sought to address this gap by modelling three distinct Contamination Indices (CI) corresponding to E. coli concentration, NEC concentration, and the NEC: E. coli concentration ratio. CIs were developed for south Ontario (115,693 km 2 ) using ∼1 million samples from ∼290,000 wells collected between 2010 and 2021. To permit modelling, CIs were linked to 50 subregion-specific variables which impact groundwater quality (e.g., well depth, aquifer type, mean daily precipitation volumes); Generalized Additive Models (GAM) were subsequently developed and associated non-linear partial effects were calculated. Findings suggest NEC concentrations may appropriately indicate a source’s long-term potential for generalized contamination, as the NEC model exhibited high deviance explained (91.9%) due to significant associations (p < 0.05) with factors influencing and/or representing groundwater recharge. A daily summer rainfall “tipping point” was identified, with volumes >3mm being associated with NEC concentration reductions (p < 0.0001), potentially due to subsoil saturation and/or aquifer contamination dilution. Regions with predominantly deep wells in bedrock aquifers were associated (p < 0.0001) with low NEC: E. coli ratios, i.e., localized contamination mechanisms (e.g., contaminant bypass or short-circuiting) likely dominate in these regions. The presumption that deeper aquifers/wells are “safer” may thus be due for reconsideration. The importance of understanding and inferring contamination mechanisms cannot be overstated, as it serves as a foundation for evidence-based source protection and testing recommendations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".