Serological Evidence of Flaviviruses and Alphaviruses in Livestock and Wildlife in Trinidad
Bibliographic record
Abstract
Seroprevalence rates of selected arboviruses in animal populations in Trinidad were determined using serum samples collected between 2006 and 2009 from horses (n=506), cattle (n=163), sheep (n=198), goats (n=82), pigs (n=184), birds (n=140), rodents (n=116), and other vertebrates (n=23). The sera were screened for antibodies to West Nile virus (WNV), St. Louis encephalitis virus (SLEV), Ilheus virus (ILHV), Bussuquara virus (BSQV), Venezuelan equine encephalitis virus (VEEV), eastern equine encephalitis virus (EEEV), and western equine encephalitis virus (WEEV), using hemagglutination inhibition assay (HIA) and epitope-blocking enzyme-linked immunosorbent assays (ELISA). Antibodies to SLEV were detected in a total of 49 (9.7%) horses, 8 (4.9%) cattle, 1 (1.2%) goat, 2 (1.4%) wild birds, and 3 (2.2%) wild rodents by both methods. In contrast, antibodies to EEEV, VEEV, and WNV were detected only in horses, at rates of 4.3%, 0.8%, and 17.2%, respectively, by ELISA, and IgM capture ELISA was WNV-positive in 3 (0.6%) of these sera. Among locally bred unvaccinated horses that had never left Trinidad, seroprevalence rates against WNV were 12.1% and 17.2% by ELISA and HIA, respectively. The presence of WNV- and SLEV-specific antibodies in a representative sample of horse sera that were both ELISA- and HIA-seropositive was confirmed by plaque reduction neutralization testing (PRNT). Antibodies to ILHV and BSQV were not detected in any of the serum samples tested (i.e., sera from horses, other livestock, and wild birds in the case of ILHV, and wild mammals in the case of BSQV). The data indicate the presence of WNV in Trinidad, and continuing low-level circulation of SLEV, EEEV, and VEEV.
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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 | 0.001 | 0.001 |
| 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".