Meta-Analysis of the Reduction of Norovirus and Male-Specific Coliphage Concentrations in Wastewater Treatment Plants
Bibliographic record
Abstract
Human norovirus (NoV) is the leading cause of foodborne illness in the United States and Canada. Wastewater treatment plant (WWTP) effluents impacting bivalve mollusk-growing areas are potential sources of NoV contamination. We have developed a meta-analysis that evaluates WWTP influent concentrations and log10 reductions of NoV genotype I (NoV GI; in numbers of genome copies per liter [gc/liter]), NoV genotype II (NoV GII; in gc/liter), and male-specific coliphage (MSC; in number of PFU per liter), a proposed viral surrogate for NoV. The meta-analysis included relevant data (2,943 measurements) reported in the scientific literature through September 2013 and previously unpublished surveillance data from the United States and Canada. Model results indicated that the mean WWTP influent concentration of NoV GII (3.9 log10 gc/liter; 95% credible interval [CI], 3.5, 4.3 log10 gc/liter) is larger than the value for NoV GI (1.5 log10 gc/liter; 95% CI, 0.4, 2.4 log10 gc/liter), with large variations occurring from one WWTP to another. For WWTPs with mechanical systems and chlorine disinfection, mean log10 reductions were -2.4 log10 gc/liter (95% CI, -3.9, -1.1 log10 gc/liter) for NoV GI, -2.7 log10 gc/liter (95% CI, -3.6, -1.9 log10 gc/liter) for NoV GII, and -2.9 log10 PFU per liter (95% CI, -3.4, -2.4 log10 PFU per liter) for MSCs. Comparable values for WWTPs with lagoon systems and chlorine disinfection were -1.4 log10 gc/liter (95% CI, -3.3, 0.5 log10 gc/liter) for NoV GI, -1.7 log10 gc/liter (95% CI, -3.1, -0.3 log10 gc/liter) for NoV GII, and -3.6 log10 PFU per liter (95% CI, -4.8, -2.4 PFU per liter) for MSCs. Within WWTPs, correlations exist between mean NoV GI and NoV GII influent concentrations and between the mean log10 reduction in NoV GII and the mean log10 reduction in MSCs.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.048 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".