Primary surveys on molecular epidemiology of bovine viral diarrhea virus 1 infecting goats in Jiangsu province, China
Bibliographic record
Abstract
BACKGROUND: Bovine viral diarrhea virus (BVDV) is a pathogen of domestic and wildlife animals worldwide and is associated with several diseases. In China, there are many reports about genotyping of BVDV strains originated from cattle and pigs, and some of them focused on the geographical distributions of BVDV. Currently, the goat industry in Jiangsu province of China is under going a rapid expansion. Most of these goat farms are backyard enterprises and in close proximity to pig and cattle farms. However, there was very limited information about BVDV infections in goats. The objective of this study was to assess the frequency of BVDV infections of goats, the relationship of these infections to clinical signs and determine what BVDV genotypes are circulating in Jiangsu province. RESULTS: From 236 goat sera collected from six regions in Jiangsu province between 2011 and 2013, BVDV-1 was identified in 29 samples from the five regions by RT-PCR. The BVDV-1 infections occurred with/without clinical signs. Eight different BVDV-1 strains were identified from these positive samples based on the 5'-untranslated region (5'-UTR) sequences, and further clustered into four BVDV-1 subtypes on the phylogenetic analysis. Three were BVDV-1b, two BVDV-1m, two BVDV-1o, and one BVDV-1p, respectively. CONCLUSIONS: To our knowledge, this is the first report to investigate the occurrence of BVDV and the genotypes of BVDV infecting goats in China. The results indicated that BVDV-1 infections were indeed present and the viruses were with genetic variations in Chinese goat herds. The information would be very useful for prevention and control of BVDV-1 infections in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".