Retrospective analysis of Senecavirus A emergence in diagnostic samples from 2010-2021 in Thailand
Bibliographic record
Abstract
This study investigates Senecavirus A (SVA) in Thai swine using 932 clinical samples, comprising 185 serum samples and 747 vesicular fluid and lesion tissue samples. These samples were collected from suspected cases between 2010 to 2021 at pig farms in the northern, western, and central regions of Thailand by the Swine Viral Evolution and Vaccine Development Research Unit (SVEVR) at Chulalongkorn University, Bangkok, Thailand. SVA was first detected in Thailand in 2016 and has been widespread since then, with PCR-based molecular detection showing an average prevalence of 16.3% for SVA, 30.7% for foot and mouth disease virus (FMDV), and 7.5% for co-infections. The SVA isolates are closely related to the Canadian strain 11-55910-3, sharing 63% genetic similarity. Commercial ELISA tests for antibody detection indicated co-infections during SVA outbreaks from 2016 to 2021.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".