Suspended Solids\nand Optimal RNase Inhibitors Impact\nthe Partitioning and Decay of SARS-CoV‑2 in Wastewater
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, can be monitored in wastewater due to its presence in human fecal matter. While wastewater surveillance programs for COVID-19 have already been implemented in many countries, fundamental questions remain regarding the distribution and decay of both SARS-CoV-2 and the common fecal indicator pepper mild mottle virus (PMMoV). In this study, for wastewater samples at 4 °C, the first-order decay rate constant (<i>k</i>) for a spiked coronavirus (HCoV 229E) was greater in mixtures with low TSS concentrations (0.373 ± 0.021 day<sup>–1</sup>) than in those with high concentrations (0.204 ± 0.014 day<sup>–1</sup>), which was consistent with measurements of the extended activity of RNases. Increasing the concentration of nontargeted and targeted RNase inhibitors revealed that the loss of the viral signal from the extraction is mainly due to the activity of RNA-degrading enzymes. A reanalysis of wastewater samples from Quebec City, Canada, with a 10× concentration of β-mercaptoethanol, a nontargeted RNase inhibitor, achieved an increase in SARS-CoV-2 and PMMoV concentrations. This investigation revealed further optimization avenues for improving the detection limit of SARS-CoV-2 in wastewater and enhancing the efficiency of wastewater surveillance programs, particularly in times of low viral prevalence.
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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) | 0.000 | 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) | 0.005 | 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".