Persistence of Human Norovirus (GII) in Surface Water: Decay Rate Constants and Inactivation Mechanisms
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Human norovirus (HuNoV) is an important cause of acute gastroenteritis and can be transmitted by water exposures, but its persistence in water is not well understood. Loss of HuNoV infectivity in surface water was compared with persistence of intact HuNoV capsids and genome segments. Surface water from a freshwater creek was filter-sterilized, inoculated with HuNoV (GII.4) purified from stool, and incubated at 15 or 20 °C. We measured HuNoV infectivity via the human intestinal enteroid system and HuNoV persistence via reverse transcription-quantitative polymerase chain reaction assays without (genome segment persistence) or with (intact viral capsid persistence) enzymatic pretreatment to digest naked RNA. For infectious HuNoV, results ranged from no significant decay to a decay rate constant ( “k ”) of 2.2 day –1 . In one creek water sample, genome damage was likely a dominant inactivation mechanism. In other samples from the same creek, loss of HuNoV infectivity could not be attributed to genome damage or capsid cleavage. The range in k and the difference in the inactivation mechanism observed in water from the same site could not be explained, but variable constituents in the environmental matrix could have contributed. Thus, a single k may be insufficient for modeling virus inactivation in surface waters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".