Prevalence of poultry diseases in Gazipur district of Bangladesh
Bibliographic record
Abstract
This study was designed with a view to investigate the prevalence of poultry diseases in Gazipur district of Bangladesh. A total of 679 poultry birds (313 layers, 338 broilers and 28 cockrels) either dead or live were brought for diagnosis of diseases at Gazipur Sadar Upazilla Veterinary Hospital. The diseases were diagnosed on the basis of history, clinical signs and post-mortem findings. The diseases encountered in layers were bacterial diseases 52.29% (salmonellosis 38.56%, colibacillosis 6.7%, fowl cholera 4.79% and necrotic enteritis 1.60%), viral diseases 23.95% (avian influenza 2.56%, Newcastle disease 16.61%, infectious bronchitis 3.19% and avian leucosis 0.64%), mycoplasmal disease (mycoplasmosis 14.70%) and protozoal disease (coccidiosis 5.75%). Salmonellosis was most prevalent disease in age group of >20 weeks, while Newcastle disease most common in 8 to 20 weeks of age group. In case of broiler, bacterial diseases 28.99% (salmonellosis 21.30% and colibacillosis 7.69%), viral diseases 53.24% (infectious bursal disease 28.99%, Newcastle disease 8.87% and infectious bronchitis 15.38%), mycoplasmal disease (mycoplasmosis 7.1%) and protozoal disease (coccidiosis 6.5%). In cockrels, the most prevalent disease was colibacillosis 35.71% followed by salmonellosis 28.57%, Newcastle disease 14.28% and mycoplasmosis14.28%. So among the diseases, salmonellosis is most prevalent disease followed by infectious bursal disease and mycoplasmosis in different kinds of poultry of Gazipur district of Bangladesh.Asian J. Med. Biol. Res. March 2016, 2(1): 107-112
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".