Detection of SARS-CoV-2 in wastewater in Halifax, Nova Scotia, Canada, using four RT-qPCR assays
Bibliographic record
Abstract
Wastewater-based surveillance methods have been implemented in several countries as a tool for monitoring SARS-CoV-2 at a community scale. A variety of methods have been used for concentrating, extracting, and detecting the virus, with no clear consensus on the most effective approach. In this note, we report preliminary findings from a study that is tracking SARS-CoV-2 in wastewater in Halifax, Nova Scotia, with a specific focus on the use of four reverse transcriptase quantitative PCR (RT-qPCR) assays for detecting the virus in wastewater. We were able to detect the virus in wastewater samples during the initial rise of cases in the Halifax region in early November 2020. Levels of the targeted SARS-CoV-2 gene fragments increased and fell in response to reported cases of COVID-19. The CDC N1 and E RT-qPCR assays demonstrated greater relative sensitivity than the CDC N2 and N3 assays for detection of SARS-CoV-2 in raw sewage samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".