Micro-Agglutination Test (MAT) based sero-epidemiological study of salmonellosis in dogs
Bibliographic record
Abstract
Sera samples collected from 575 dogs of different breeds, age, sex and places were screened for Salmonella antibodies using micro-agglutination test (MAT) during 2004–05. Agglutinin titres of = 80 and = 320 against somatic (‘O’) and flagellar (‘H’) antigens respectively were considered positive. An overall sero-prevalence of Salmonella agglutinin against ‘O’ and ‘H’ antigens in dogs were 73.22 and 53.91%, respectively. The most prevalent agglutinins in dog serum among ‘O’ antigen were against 3, 10 (60.35%), while the most prevalent agglutinins among ‘H’ antigen were against e, n, x (40.87%). Prevalence of ‘O’ and ‘H’ agglutinins with respect to breed, sex, age and place of dogs was determined. Breed wise distribution of positive agglutinins titres against both ‘O’ and ‘H’ antigens were 90.79 and 65.79%, respectively in Labrador dog. Age wise prevalence of positive agglutinin titres against ‘O’ and ‘H’ antigens were found to increase with age. Sex of the dog had no significant influence on the prevalence of agglutinin titres against Salmonella ‘O’ and ‘H’ antigens. The prevalence of agglutinins in dog sera against ‘O’ and ‘H’ antigens were maximum 91.43 and 71.43% in dogs of National Training Centre for Dogs (NTCD), Tekanpur, Gwalior.
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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 | 0.001 |
| 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) | 0.000 | 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".