Wastewater solids drive comparability of sampling methods for SARS-CoV-2 wastewater and environmental surveillance
Bibliographic record
Abstract
Passive sampling has proven to be a reliable and cost-effective method in wastewater and environmental surveillance (WES) during the COVID-19 pandemic. In passive sampling, wastewater solids were collected from the wastewater networks, homogenized in solutes and analyzed the supernatant to measure the SARS-CoV-2 RNA concentrations in wastewater. However, the direct impact of wastewater solids content collected via passive sampling on SARS-CoV-2 RNA measurements has not been previously evaluated. In this study, we analyzed wastewater solids collected using Auto, Torpedo, COSCa-ball samplers, and primary sludge samples from a wastewater treatment plant to measure SARS-CoV-2 RNA concentrations in wastewater. Results showed significant variation (p < 0.05) in wastewater solids content (i.e., TS and VS) across Auto, Torpedo, COSCa-ball samplers, and primary sludge samples. Despite differences in solids content, SARS-CoV-2 RNA concentrations in wastewater solids from passive samplers can be effectively compared (p > 0.05) to autosampler and primary sludge samples. To evaluate the influences of wastewater solids content on SARS-CoV-2 RNA measurement, we used a linear mixed-effects model. The model demonstrated that wastewater solids content had no direct effect on SARS-CoV-2 RNA measurements across the sampling methods and primary sludge samples. Overall, this study established a standardized experimental approach for implementing passive samplers as a viable alternative to conventional autosampler in WES for emerging pathogens. • Wastewater solids were evaluated for SARS-CoV-2 RNA measurements across sampling methods. • Wastewater solids content varied among Auto, Torpedo, COSCa-ball sampler and primary sludge samples. • SARS-CoV-2 RNA concentrations in solid mass fraction or PMMoV-normalized unit from passive samplers can be compared to autosampler and primary sludge samples. • Wastewater solids content had no direct influence on SARS-CoV-2 RNA measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".