Comparison of Detecting and Quantitating SARS-CoV-2 in Wastewater Using Moderate-Speed Centrifuged Solids versus an Ultrafiltration Method
Bibliographic record
Abstract
Mounting evidence suggests that solids are a reliable matrix for SARS-CoV-2 detection in wastewater, yet studies comparing solids-based methods and common concentration methods using the liquid fraction remain limited. In this study, we developed and optimized a method for SARS-CoV-2 detection in wastewater using moderate-speed centrifuged solids and evaluated it against an ultrafiltration reference method. SARS-CoV-2 was quantified in samples from 12 wastewater treatment plants from Alberta, Canada, using RT-qPCR targeting the N2 and E genes. PCR inhibition was examined by spiking salmon DNA. The effects of using different amounts of solids, adjusting the sample pH to 9.6–10, and modifying the elution volume at the final step of RNA extraction were evaluated. SARS-CoV-2 detection rate in solids from 20 mL of wastewater showed no statistically significant difference compared to the ultrafiltration method (97/139 versus 90/139, p = 0.26, McNemar’s mid-p test). The optimized wastewater solids-based method had a significantly lower rate of samples with PCR inhibition versus ultrafiltration (3% versus 9.5%, p = 0.014, Chi-square test). Our optimized moderate-speed centrifuged solids-based method had similar sensitivity when compared to the ultrafiltration reference method but had the added advantages of lower costs, fewer processing steps, and a shorter turnaround time.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".