Supplementary materials for the article reporting Pacific Oyster Nidovirus 1 (PONV1)
Bibliographic record
Abstract
Here, we provide supplementary materials, including phylogenetic trees, sequences, and relevant codes, for the article (Zhong et al. 2025; https://doi.org/10.1073/pnas.2426923122) reporting Pacific Oyster Nidovirus 1 (PONV1), detected in farms where Pacific oysters experiencing mass mortality events in British Columbia, Canada. Contents of the sequence file:Rdrp_containing_contig_unique_vOTU_189.fasta Representative nucleotide sequences of 189 RdRp-containing RNA virus operational taxonomic units (vOTUs) identified from the Pacific oyster samples (ENA accession numbers: ERR13334386 to ERR13334481).PONV1_segment_1.fasta Nucleotide sequences of segment 1 from PONV1. NCBI accession number: PQ030832.PONV1_segment_2.fasta Nucleotide sequences of segment 2 from PONV1. NCBI accession number: PV579160.PONV1-like_virus_segment_1.fasta Nucleotide sequences of segment 1 from PONV1-like virus 1 through 15. NCBI accession numbers: BK068944 to BK068958.PONV1-like_virus_segment_2.fasta Nucleotide sequences of segment 2 from PONV1-like virus 1 through 15. NCBI accession numbers: PV928314 to PV928328. Reference:Zhong KX, Chan AM, Miller KM, Saunders R, and Suttle CA (2025) Evolutionarily divergent nidovirus with an exceptionally large genome identified in Pacific oysters undergoing mass mortality. Proceedings of the National Academy of Sciences USA, 122: e2426923122. https://doi.org/10.1073/pnas.2426923122
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.782 | 0.358 |
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".