A Re-Evaluation of African Swine Fever Genotypes Based on p72 Sequences Reveals the Existence of Only Six Distinct p72 Groups
Bibliographic record
Abstract
The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".