Temporal Variation of SARS-CoV-2 Levels in Wastewater from a Meat Processing Plant
Bibliographic record
Abstract
Wastewater-based surveillance (WBS) on SARS-CoV-2 has been proved to be an effective approach to estimate the prevalence of COVID-19 in communities and cities. However, its application was overlooked at smaller scale, such as a single facility. Meat processing plants are hotspots for COVID-19 outbreaks due to their unique environment that are favorable for the survival and persistence of SARS-CoV-2. This is the first known WBS study in meat processing plants. The goal was to understand the temporal variation of the SARS-CoV-2 levels in wastewater from a meat processing plant in Canada during a three-month campaign and to find any correlation with clinically confirmed cases in the surrounding city area. Higher SARS-CoV-2 concentrations and detection frequencies were observed in the solid fraction compared to the liquid fraction of the wastewater. The viruses can be preserved in the solid fraction of wastewater for up to 12 days. The wastewater virus level did not correlate to the city-wide COVID-19 cases due to the unmatching scales. WBS on SARS-CoV-2 in meat processing plants can be useful for identifying COVID-19 outbreaks in the facility and serve as an effective alternative when resources for routine individual testing are not available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".