Nonrubber Cage Mats and Dirty Cages Are Risk Factors for Subclinical Mastitis in Dairy Cows at Karanganyar, Tani Makmur Lumajang
Bibliographic record
Abstract
Mastitis is an inflammatory response caused by microorganisms or physical trauma that occurs in the udder. This study used an epidemiological survey approach in which the prevalence of subclinical mastitis and the factors that affect subclinical mastitis were calculated. The subclinical mastitis test uses indirect tests with California mastitis test reagents and direct tests with breed test methods. Risk factors for hygiene and bedding materials were identified via questionnaire aids. Data processing was performed via descriptive data and the chi-square test followed by relative risk correlation. The results of the present study revealed that the prevalence of subclinical mastitis in the Karanganyar milk shelter 2 cooperative of the Tani Makmur village unit, Lumajang, was 25.2% in the California mastitis test and 30.08% in the breed test. Cages made of nonrubber (p<0.05; relative risk [RR] = 7.519; 95% confidence interval (CI): 3,953–14,298]) in the breed test and (p<0.05; relative risk [RR] = 5.9; 95% confidence interval (CI): 3,019–11,559]) in the California mastitis test. Cleanliness with a gross value has (p<0.05; relative risk [RR] = 3.701; 95% confidence interval (CI): 2,360–5,803]) in the breed test and (p<0.05; relative risk [RR] = 4.01; 95% confidence interval (CI): 2,3576,788]). This is because materials that have a rough surface can increase the degree of trauma to the udder. In summary, the dirtier cage presented a higher rate of subclinical mastitis infection than did the clean cage. Dirty cages can be a place for the growth of pathogenic microorganisms that are detrimental to livestock.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".