Serosurvey of peste des petits ruminants virus in small ruminants from different agro-ecological zones of Nigeria
Bibliographic record
Abstract
Peste des petits ruminants, caused by the peste des petits ruminants virus (PPRV), is a highly contagious and economically important transboundary viral disease of domestic and wild small ruminants and a major hindrance to small-ruminant production in Nigeria. The seroprevalence and distribution of PPRV antibodies in small ruminants in rural households, farms, live animal markets and slaughter slabs across the six different agro-ecological zones of Nigeria were determined. A total of 4548 serum samples from 3489 goats and 1059 sheep were collected in 12 states. A PPRV competitive enzyme-linked immunosorbent assay was used to test the samples and the data analysed with R statistical software version 3.0.1. The study animals included all ages and both sexes. The overall prevalence estimate of sera positive for PPRV antibodies was 23.16% (n = 1018 positive samples per 4548 total samples, 95% confidence interval: 21.79% - 24.57%). There were significant differences in the seroprevalence between the states (p = 0.001). Taraba State had the highest seroprevalence of 29.51%, whilst the lowest seroprevalence of 14.52% was observed in Cross River State. There were no significant differences in the PPRV seroprevalence between male and female animals (p = 0.571), age (p = 0.323) and between species (p = 0.639). These data indicate the current seroprevalence to PPRV in the small-ruminant population in Nigeria.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".