Udder quarter risk factors associated with prevalence of bovine clinical mastitis
Bibliographic record
Abstract
A cross sectional study was carried out to estimate prevalence of clinical mastitis on udder quarters level and to determinate the quarter risk factors associated with the development of clinical mastitis during lactation. The individual risk factors included assessments of parity, season of year when case of clinical mastitis was occurred, conformation characteristics of udder quarters and teats and distance from front and rear teat end to the fl oor. Cows with clinical mastitis were detected by clinical examination of the udder quarters and determination of abnormalities in milk. The quarter level prevalence of clinical mastitis was 15.06% per lactation, out of which 3.32% were front left, 3.10% front right, 4.28% rear left and 4.28% were rear right quarters. The prevalence of udder quarters affected with clinical mastitis tended to increased with increasing the parity, from cows in fi rst to the third parity, and then begins to decline slightly. The rear quarters frequently manifested form of clinical mastitis (49.39%) in relation to the front one (33.04%), and in 17.55% of the cases there were affected e ither, front and rear quarters. In the most cases of clinical mastit there was affected only one quarter of the mammary gland (74.35%), two quarters in 20.13%, three quarters to 3.61% and four quarters were affected in 1.89% of the cases of clinical mastitis. The method of General Linear Model, unvaried procedure, revealed that prevalence of clinical mastitis on quarter level significantly (p<0.01) differed with the season of year when case of clinical mastitis was occured and scoring categories for position of rear udder quarters.
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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 | 0.000 | 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".