SARS-CoV-2 variant detection with ADSSpike
Bibliographic record
Abstract
The SARS-CoV-2 coronavirus pandemic has been an unprecedented challenge to global pandemic response and preparedness. With the continuous appearance of new SARS-CoV-2 variants, it is imperative to implement tools for genomic surveillance and diagnosis in order to decrease viral transmission and prevalence. The ADSSpike workflow was developed with the goal of identifying signature SNPs from the S gene associated with SARS-CoV-2 variants through amplicon deep sequencing. Seventy-two samples were sequenced, and 30 mutations were identified. Among those, signature SNPs were linked to 2 Zeta-VOI (P.2) samples and one to the Alpha-VOC (B.1.17). An average depth of 700 reads was found to properlycorrectly identify all SNPs and deletions pertinent to SARS-CoV-2 mutants. ADSSpike is the first workflow to provide a practical, cost-effective, and scalable solution to diagnose SARS-CoV-2 VOC/VOI in the clinical laboratory, adding a valuable tool to public health measures to fight the COVID-19 pandemic for approximately $41.85 USD/reaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".