Metagenomic sequencing of municipal wastewater provides a near-complete SARS-CoV-2 genome sequence identified as the B.1.1.7 variant of concern from a Canadian municipality concurrent with an outbreak
Bibliographic record
Abstract
Abstract Laboratory-based wastewater surveillance for SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, can be conducted using RT-qPCR-based screening of municipal wastewater samples. Although it provides rapid viral detection and can inform SARS-CoV-2 abundance in wastewater, this approach lacks the resolution required for viral genotyping and does not support tracking of viral genome evolution. The recent emergence of several variants of concern, a result of mutations across the genome including the accrual of important mutations within the viral spike glycoprotein, has highlighted the need for a method capable of detecting the cohort of mutations associated with these and newly emerging genotypes. Here we provide an innovative methodology for the recovery of a near-complete SARS-CoV-2 sequence from a wastewater sample collected from across Canadian municipalities including one that experienced a significant outbreak attributable to the SARS-CoV-2 B.1.1.7 variant of concern. Our results demonstrate that a combined interrogation of genome consensus-level sequences and alternative alleles enables the identification of a SARS-CoV-2 variant of concern and the detection of a new allele within a viral accessory gene that may be representative of a recently evolved B.1.1.7 sublineage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".