Exploring the diversity of coronavirus in sewage during COVID-19 pandemic: Don't miss the forest for the trees
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, the use of next generation sequencing (NGS) has proved to be an important tool for the genetic characterization of SARS-CoV-2 from clinical samples. The use of different available NGS tools applied to wastewater samples could be the key for an in-depth study of the excreted virome, not only focusing on SARS-CoV-2 circulation and typing, but also to detect other potentially pandemic viruses within the same family. With this aim, 24-hours composite wastewater samples from March and July 2020 were sequenced by applying specific viral NGS as well as target enrichment NGS. The full virome of the analyzed samples was obtained, with human Coronaviridae members (CoV) present in one of those samples after applying the enrichment. One contig was identified as HCoV-OC43 and 8 contigs as SARS-CoV-2. CoVs from other animal hosts were also detected when applying this technique. These contigs were compared with those obtained from contemporary clinical specimens by applying the same target enrichment approach. The results showed that there is a co-circulation in urban areas of human and animal coronaviruses infecting domestic animals and rodents. NGS enrichment-based protocols might be crucial to describe the occurrence and genetic characteristics of SARS-CoV-2 and other Coronaviridae family members within the excreted virome present in wastewater.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".