Additional file 2 of Decoding the RNA viromes in rodent lungs provides new insight into the origin and evolutionary patterns of rodent-borne pathogens in Mainland Southeast Asia
Bibliographic record
Abstract
Additional file 1. Table S1. Samples of the 30 animal species used in this study and the countries (provinces) and dates of collection. Table S2. Overview of virus-associated reads. Table S3. Overview of sequence reads from mammal related viral families and unclassified RNA viruses. Table S4. Origin and accession number of viruses identified in this study. Table S5. Nucleotide sequence identity of novel rodent hantaviruses and known hantaviruses within the L, M, and S segment. Table S6. Nucleotide sequence identity of novel rodent PhleVs and known PhleVs within the L open reading frame. Table S7. Nucleotide sequence identity of novel rodent arenaviruses and known arenaviruses (AreV) in the L-, G- and N-encoding regions. Table S8. Nucleotide sequence identity of novel rodent rhabdoviruses and known rhabdoviruses (RhaV) in the L protein-encoding regions. Table S9. Nucleotide sequence identity of novel rodent paramyxoviruses and known paramyxoviruses (ParaV) in the L protein-encoding regions. Table S10. Nucleotide sequence identity of novel rodent and known hepaci-, pegi- and pestviruses. Table S11. ORF1b nucleotide sequence identity of novel rodent and known arteriviruses. Table S12. Nucleotide sequence identity of novel rodent and known coronaviruses (CoVs) in the RdRP-encoding region. Table S13. Nucleotide sequence identity of novel rodent and known hepeviruses (HEVs) in the ORF1 region. Table S14. Nucleotide sequence identity of novel rodent and known picornaviruses (PicoVs) in the RdRP-encoding region. Table S15. Nucleotide sequence identity of novel rodent and known astroviruses (AstroVs) in the RdRP-encoding region. Table S16. Nucleotide sequence identity of novel rodent unclassified RNA viruses and known viruses in the RdRP-encoding region.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.799 | 0.162 |
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".