A Practical Guide to the Taxonomy, Classification, and Characterization of Clinically Important Viruses
Bibliographic record
Abstract
Abstract This chapter focuses on the characterization, classification, and taxonomy of viruses that infect humans, both pathogens that infect humans alone and those that infect humans but may also infect other mammalian or nonmammalian genera and species. While other forms of life encode genetic information within double‐stranded DNA (dsDNA), viruses encode their genetic information within genomes that may be composed of single‐stranded RNA, double‐stranded RNA, single‐stranded DNA, and dsDNA. The Baltimore classification, a nonhierarchical approach, categorizes viruses into seven groups and is based on the genome present in virions and type of replication. Classification of viruses below the level of species is not standardized across all species by the International Committee on Taxonomy of Viruses, but in some cases, subspecies standardization does occur. Between 2016 and 2021, a series of changes have occurred in the taxonomy of viruses that infect humans.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.052 |
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".