Impact of various milking equipment on incidence of mastitis in dairy herd
Bibliographic record
Abstract
It was found that the lowest percentage of cows with mastitis was recorded in winter: 10.3% (P≤0.001) when milking with ‘De Laval’ equipment and 17.7% (P≤0.001) when milking with the ADM-8A unit. The highest incidence of mastitis in cows was observed in summer: 21.0% (P≤0.001) and 27.1% (P≤0.001), respectively, when milking cows at milking parlors ‘De Laval’ and ADM-8A. It was found that, when using milking equipment ADM-8A, adult cows are 2.1-1.7 times more prone to mastitis compared to cows of the 1st and 2nd calving. When milking cows with the ‘De Laval’ unit, the incidence of mastitis in adult cows is 4.3-1.1 times higher than in cows of the 1st and 2nd calving. Gentle milking mode on the ‘De Laval’ equipment allowed to increase the number of completely healthy animals to 74%, which is 9.6% more than when using the milking unit ADM-8A (64.4%), and to reduce the incidence of clinical mastitis in cows by 3.4 times. It has been established that in cows with disorders in the udder the content of somatic cells in the secretion of the udder significantly changes (with a high degree of correlation) in all periods of the functional state of the body. Thus, during subclinical mastitis r=l0.72 (P≤0.001) and udder irritation r=l0.58 (P≤0.05). At the beginning of lactation, subclinical mastitis is accompanied with significant changes in the activity of enzymes: muramidase r=l0.84 (P≤0.001), lactoperoxidase r=l0.65 (P≤0.01) and lactoferrin r=l0.66 (P≤0.01).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".