Seroprevalence of Salmonella and Mycoplasma gallisepticum infection in chickens in Rajshahi and surrounding districts of Bangladesh
Bibliographic record
Abstract
A serological survey on the prevalence of antibodies against Salmonella and Mycoplasma gallisepticum (MG) was carried out in layer chickens in Rajshahi and surrounding districts of Bangladesh. A total of 605 sera samples were examined by rapid plate agglutination (RPA) test using commercial Salmonella and MG antigens to determine the Salmonella and MG specific antibodies. Out of 605 sera samples 14.05% had single Salmonella, 45.12% had single MG and 11.24% had their concurrent infection. Prevalence of Salmonella was recorded the highest (37.60%) in adult compared to young (16.66%). On the contrary, MG and their concurrent infections were recorded the highest (71.66% and 13.33%) in young compared to adult (50.40% and 10.40%). The prevalence of Salmonella, MG and their concurrent infections were recorded the highest (34.28%, 68.57% and 17.14%) in large flocks compared to small flocks (21.25%, 50% and 8.75%). The prevalence of Salmonella infection was the highest (30.37%) in summer followed by winter (23.69%), rainy (25%) and autumn (23.33%). The prevalence of MG infection was the highest (61.58%) in winter followed by autumn (56.88%), rainy (55%) and summer (49.63%). Whereas, their concurrent infection was the highest (12.11%) in winter followed by summer (11.85%), rainy (10.83%) and autumn (10%).
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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 | 0.001 | 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".