Patterns of seroprevalence of feline viruses among domestic cats (<i>Felis catus</i>) and Pallas’ cats (<i>Otocolobus manul</i>) in Daursky Reserve, Russia
Bibliographic record
Abstract
Few data are available on the prevalence of feline viruses in the wild and little is known about natural sources of infections. The aim of this study was to estimate patterns of seroprevalence to feline viruses (feline immunodeficiency virus (FIV), feline calicivirus (FCV), feline panleukopenia virus (FPV), feline herpesvirus (FHV), and feline leukemia virus (FeLV)) in two cat species, domestic cats (Felis catus L., 1758) (n = 61) and Pallas’ cats (Otocolobus manul (Pallas, 1776)) (n = 24), living in the same area, in Daursky Reserve, Russia. Our results indicate that four of five viruses are circulating in the study area, with the exception of FHV. The pattern of FCV and FPV prevalence differed from FIV and FeLV. FCV and FPV seroprevalence did not depend on the sex and predominated in the group of cats living in the village (76% and 55%, respectively). No Pallas’ cats were seropositive to these viruses. The prevalence of FIV and FeLV were similar in areas with different cat densities (at the stations (16% for both viruses) and in the village (16% for both viruses)). The patterns of seroprevalence between species testify to the low rate of interspecific contacts. In Pallas’ cats, we found the presence of antibodies to FIV to be 5% and antigens of FeLV to be 5%, pathogens for which transmission demand close direct contacts between animals (mainly aggressive and (or) sexual contact), which is typical in the breeding season. Arid climate may also reduce patterns of viral prevalence in the study area, decreasing the risk of infection for both cat 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 | 0.000 | 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".