Occurrence of Human Norovirus GII and Human Enterovirus in Ontario Source Waters
Bibliographic record
Abstract
Norovirus and Enterovirus are common human viral pathogens found in watersources. Despite causing gastroenteritis outbreaks, most jurisdictions, including Ontario, do not monitor for enteric viruses in waters. The objective of this thesis was to monitor the presence of human Norovirus and Enterovirus in Ontario source waters intended for drinking. Two untreated source water types (river and ground water) were sampled routinely and following precipitation and snow melt events between January 2015 and April 2016. Physical, chemical, microbiological, and meteorological data were collected, coinciding with sampling events. A modified USEPA Method 1615 was applied to detect and quantify viruses and logistic regression was used to examine relationships between virus presence and environmental parameters. Norovirus was detected in 41% of river water and 33% of groundwater samples. Enterovirus was detected in 18% of river water and 29% of groundwater samples. No correlations between virus detection and environmental parameters were found.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".