Total coliform and Escherichia coli contamination in rural well water: analysis for passive surveillance
Bibliographic record
Abstract
With increasing stress on our water resources and recent waterborne disease outbreaks, understanding the epidemiology of waterborne pathogens is crucial to build surveillance systems. The purpose of this study was to explore techniques for describing microbial water quality in rural drinking water wells, based on spatiotemporal analysis, time series analysis and relative risk mapping. Tests results for Escherichia coli and coliforms from private and small public well water samples, collected between 2004 and 2012 in Alberta, Canada, were used for the analysis. Overall, 14.6 and 1.5% of the wells were total coliform and E. coli-positive, respectively. Private well samples were more often total coliform or E. coli-positive compared with untreated public well samples. Using relative risk mapping we were able to identify areas of higher risk for bacterial contamination of groundwater in the province not previously identified. Incorporation of time series analysis demonstrated peak contamination occurring for E. coli in July and a later peak for total coliforms in September, suggesting a temporal dissociation between these indicators in terms of groundwater quality, and highlighting the potential need to increase monitoring during certain periods of the year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".