Summary of taxonomy changes ratified by the International Committee on Taxonomy of Viruses (ICTV) from the Bacterial Viruses Subcommittee, 2025
Bibliographic record
Abstract
This article summarises the activities of the International Committee on Taxonomy of Viruses Bacterial Viruses Subcommittee, detailing developments in the classification of bacterial viruses. We provide here an overview of all new, abolished, moved and renamed taxa proposed in 2024, approved by the Executive Committee, and ratified by membership vote in 2025. Through the collective efforts of 74 international contributors of taxonomy proposals in this round, 43 ratified proposals have led to the creation of one new phylum, one class, four orders, 33 families, 14 subfamilies, 194 genera and 995 species. These proposals mark significant progress in refining the taxonomy of bacterial viruses. Key updates include the creation of new orders and families that include existing taxa to better reflect genomic and evolutionary relationships. As sequencing and bioinformatics approaches continue to advance, further expansion and refinements in viral taxonomy can be anticipated in the coming years.
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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 | 0.064 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".