Early Warning Measurement of SARS-CoV‑2 Variants\nof Concern in Wastewaters by Mass Spectrometry
Bibliographic record
Abstract
Wastewater surveillance has rapidly\nemerged as an early warning\ntool to track COVID-19. However, the early warning measurement of\nnew SARS-CoV-2 variants of concern (VOCs) in wastewaters remains a\nmajor challenge. We herein report a rapid analytical strategy for\nquantitative measurement of VOCs, which couples nested polymerase\nchain reaction and liquid chromatography–mass spectrometry\n(nPCR-LC-MS). This method showed a greater selectivity than the current\nallele-specific quantitative PCR (AS-qPCR) for tracking new VOC and\nallowed the detection of multiple signature mutations in a single\nmeasurement. By measuring the Omicron variant in wastewaters across\nnine Ontario wastewater treatment plants serving over a three million\npopulation, the nPCR-LC-MS method demonstrated a better quantification\naccuracy than next-generation sequencing (NGS), particularly at the\nearly stage of community spreading of Omicron. This work addresses\na major challenge for current SARS-CoV-2 wastewater surveillance by\nrapidly and accurately measuring VOCs in wastewaters for early warning.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".