UnMuted: Defining SARS-CoV-2 lineages according to temporally consistent mutation clusters in wastewater samples
Bibliographic record
Abstract
SARS-CoV-2 lineages are defined according to placement in a phylogenetic tree, but approximated by a list of mutations based on sequences collected from clinical sampling. Wastewater lineage abundance is generally found under the assumption that the mutation frequency is approximately equal to the sum of the abundances of the lineages to which it belongs. By leveraging numerous samples collected over time, I am able to estimate the temporal trends of the abundance of lineages as well as the definitions of those lineages. This is accomplished by assuming that collections of mutations that appear together over time can be used to define lineages. Three main models are considered: One that does not imposes a temporal structure, one that includes an explicit temporal component but allows for missing lineages, and one with an explicit temporal component that attempts to estimate all lineages. It is found that the temporal trend of estimated lineage definitions approximately corresponds to the trend of lineage definitions determined by clinical samples, despite having no information from clinical 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".