Solid-liquid distribution of SARS-CoV-2 in primary effluent of a wastewater treatment plant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distributions of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and fecal viral biomarkers between solid and liquid phases of wastewater are largely unknown. Herein, distributions of SARS-CoV-2, Pepper Mild Mottle Virus (PMMoV), and F-RNA bacteriophage group II (FRNAPH-II) were determined by viral RNA RT-qPCR. Comparison of viral recovery using three conventional fractionation methods included membrane filtration, a combination of mid-speed centrifugation and membrane filtration, and high-speed centrifugation. SARS-CoV-2 partitioned to the solids fraction in greater abundance compared to liquid fractions in a combination of mid-speed centrifugation and membrane filtration and high-speed centrifugation, but not in membrane filtration method in a particular assay, while fecal biomarkers (PMMoV and FRNAPH-II) exhibited the reciprocal relationship. The wastewater fractionation method had minimal effects on the solids-liquids distribution for all viral and phage markers tested; however, viral RNA load was significantly greater in solid-liquid fractions viral RNA loads compared with the than whole-wastewater PEG precipitation. A RNeasy PowerWater Kit with PCR inhibitor removal resulted in greater viral RNA loads and lesser PCR inhibition compared to a QIAamp Viral RNA Mini Kit without PCR inhibitor removal. These results support the development of improved methods and interpretation of WBE of SARS-CoV-2. •Distribution of SARS-CoV-2 to liquid and solid portions was addressed.•Addressing PCR inhibition is important in wastewater-based epidemiology.•Fraction methods have minimal effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 it