Impacts of Event-Based Recharge on the Vulnerability of Public Supply Wells
Bibliographic record
Abstract
Dynamic recharge events related to extreme rainfall or snowmelt are becoming more common due to climate change. The vulnerability of public supply wells to water quality degradation may temporarily increase during these types of events. The Walkerton, ON, Canada, tragedy (2000) highlighted the threat to human health associated with the rapid transport of microbial pathogens to public supply wells during dynamic recharge events. Field research at the Thornton (Woodstock, ON, Canada) and Mannheim West (Kitchener, ON, Canada) well fields, situated in glacial overburden aquifers, identified a potential increase in vulnerability due to event-based recharge phenomena. Ephemeral surface water flow and local ponding containing microbial pathogen indicator species were observed and monitored within the capture zones of public supply wells following heavy rain and/or snowmelt. Elevated recharge rates beneath these temporary surface water features were estimated to range between 40 and 710 mm over two-week periods using analytical and numerical modelling based on the water level, soil moisture, and temperature data. Modelling also suggested that such events could reduce contaminant travel times to a supply well, increasing vulnerability to water quality degradation. These studies suggest that event-based recharge processes occurring close to public supply wells may enhance the vulnerability of the wells to surface-sourced contaminants.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".