Incidence of bovine subclinical mastitis in organized and unorganized farms based on somatic cell count
Bibliographic record
Abstract
Mastitis, a disease of multiple etiology, had been recognized for more than a century, and still continues to be an evergreen cause of economic loss to the dairy industry and is the costliest problem all over the world where dairying is practiced. The distribution of mastitis incidence varies from country to country. Subclinical mastitis more prevalent than clinical mastitis and its prevalence varied from herd to herd and place to place. Subclinical mastitis (SCM) is most important due to its negative impact on the economy throughout the world dairy industry. Incidence of bovine SCM was studied on dry pregnant cows of organized and unorganized farms in an around Khanapara, Guwahati, Assam. Diagnosis was based on Somatic Cell Count (SCC). A total of 30 cows were examined, of which 6 cows belonged to an organized farm and rest 24 cows to unorganized farms. Four cows (13 quarters samples) from organized farm were found positive for SCM. The incidence of SCM cow-wise was 66.67 per cent and quarter-wise 54.17 per cent. On the unorganized farms, all the milk samples from 24 cows (83 quarter samples) were positive for SCM and the incidence was 100 per cent both cow- wise and quarter-wise. The overall percentage of incidence cow-wise was 93.33 and quarter-wise 90.26 per cent. High incidence of SCM on the organized farm is implicated to small sample size, whereas on the unorganized farm the management practices leading to stress may be explained as the reason of such high incidence of SCM.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".