PRJNA1088471 Wastewater Sequences Processed
Bibliographic record
Abstract
The data come from NCBI BioProject PRJNA1088471, as originally analyzed in Overton et. al (2024). These data are short read sequences from wastewater in Toronto, Ontario. See Overton et. al (2024) for a detailed description of the genomic sequencing process. Data processing involved alignment of the short reads to the Wuhan-1 reference sequence (NC_045512) with `minimap2` v2.28, identifying the mutations relative to the reference, and recording the number of times a mutation was observed (counts) and the depth of coverage. The frequency is calculated as the counts divided by the coverage. The mutation pre-processing pipeline is available at \url{https://github.com/DASL-Lab/data-treatment-plant}, and heavily relies on the GromStole pipeline (\url{https://github.com/PoonLab/gromstole}). After being processed into counts and coverage, the data were filtered to only include mutations that are relevant to analysis. There were many mutations with either consistently low counts (possibly due to sequencing errors) or low coverage. We found all mutations that had both a frequency of at least 0.1 and a frequency below 0.9 (with a coverage at least 40) at at least two time points during the study in any location. This ensures that we have all of the mutations that were potentially part of a circulating lineage without relying on lineage definitions. Overton, Alyssa K., Jennifer J. Knapp, Opeyemi U. Lawal, et al. “Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages.” Preprint, April 9, 2024. https://doi.org/10.21203/rs.3.rs-4183960/v1.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.037 | 0.028 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.024 |
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; both teacher heads agree on what is shown here.
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".