Primary study of seroprevalence to virus pathogens in wild felids of South Primorie, Russia
Bibliographic record
Abstract
Seroprevalence to nine different virus pathogens was estimated for Russian big cats (Amur tiger (Panthera tigris altaica Temminck, 1844) and far-eastern leopard (Panthera pardus orientalis (Schiegel, 1857))) in Southern Primorie, Russia (n = 25), in 2008–2016. Serum samples from smaller cats (Eurasian lynx (Lynx lynx (Linnaeus, 1758)) and far-eastern wildcat (leopard cat) (Prionailurus bengalensis euptilurus (Elliot, 1871))) were also tested for these pathogens (n = 19) during the same period. Felids of Russian Southern Primorie showed seroprevalence to eight out of nine tested pathogens, including highly dangerous feline immunodeficiency virus, feline leukemia virus, and canine distemper virus. Antibodies to feline panleukopenia virus were found to be much more widespread in cats (45%) than antibodies to any other virus. They were detected in samples taken from tigers, leopards, and far-eastern wildcats but not lynxes. Antibodies to pseudorabies virus were detected only in Amur tiger (29%), whose main prey is the most common carrier of the virus (wild boar), unlike for the other studied cats’ species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".