Optimization of the Primary Sludge Processing Method for Wastewater Genomic Surveillance of SARS-CoV-2
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wastewater genomic surveillance (WWGS) of SARS-CoV-2 is typically performed using influent wastewater, but the approach is challenging due to degradation as well as low target concentrations in wastewater. This could be alleviated by utilizing primary sludge; however, this matrix is prone to sequencing library failures. Our study focuses on developing a robust primary sludge-based SARS-CoV-2 genome sequencing method. The study was conducted using 30 parallel influent wastewater and primary sludge samples collected during three different time periods, under three clinically predominant SARS-CoV-2 Omicron lineages in Ottawa, Canada. Results showed that our approach consistently recovered near-complete (≥90%) SARS-CoV-2 genomes from both influent wastewater and primary sludge samples. Prevalent lineage and single nucleotide variant (SNV) profiles were identical ( p > 0.05) between influent wastewater and primary sludge. Further analysis indicated that a similar ( p > 0.05) number of rare SNVs were detected between influent wastewater and primary sludge. Overall, our approach enables the sequencing of the most concentrated sources of genetic material within the wastewater matrix, providing valuable insights for public health forecasting of infectious disease prevalence beyond the COVID-19 pandemic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".