Virus occurrence in municipal groundwater sources in Quebec, Canada
Bibliographic record
Abstract
A 1 year study was undertaken on groundwater that was a source of drinking water in the province of Quebec, Canada. Twelve municipal wells (raw water) were sampled monthly during a 1 year period, for a total of 160 samples. Using historic data, the 12 sites were categorized into 3 groups: group A (no known contamination), group B (sporadically contaminated by total coliforms), and group C (historic and continuous contamination by total coliforms and (or) fecal coliforms). Bacterial indicators (total coliform, Escherichia coli, enteroccoci), viral indicators (somatic and male-specific coliphages), total culturable human enteric viruses, and noroviruses were analyzed at every sampling site. Total coliforms were the best indicator of microbial degradation, and coliform bacteria were always present at the same time as human enteric viruses. Two samples contained human enteric viruses but no fecal pollution indicators (E. coli, enterococci, or coliphages), suggesting the limited value of these microorganisms in predicting the presence of human enteric viruses in groundwater. Our results underline the value of historic data in assessing the vulnerability of a well on the basis of raw water quality and in detecting degradation of the source. This project allowed us to characterize the microbiologic and virologic quality of groundwater used as municipal drinking water sources in Quebec.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".