Adoption and consistency of application of premilking preparation in Ontario dairy herds
Bibliographic record
Abstract
Milking management practices that affect udder health have been widely studied, leading to a variety of evidence-based recommendations. Lack of adoption or inconsistency in milking practices can interfere with efforts to prevent mastitis in the herd. The study objective was to assess the variation in adoption and application consistency of important milk harvest practices between and within farms over time. During the summer of 2013, 50 herds in southern Ontario were visited twice within a month, at milking time, and a single person observed and time-recorded premilking preparation procedures. A generalized mixed model was used to partition the variance for predisinfectant contact time and preparation lag time (time between the first contact with the teats and cluster attachment), and determine the proportion of variation attributable to farms, milkers, visits, and characteristics of a cow milking. Using logistic regression, models were built to assess factors affecting adequate contact time and adequate preparation lag time, respectively. Farm, the person(s) milking, and visit number were used as random effects in both instances. In both models, farm-to-farm differences and variations between cows during a specific milking accounted for the largest part of the variability seen in both contact time (47 and 44%, respectively) and preparation lag time (40 and 36%, respectively). For both outcomes, milkers were consistent in their routines over the 2 visits (only 9 and 3.1% of total variance for contact and preparation lag time, respectively). Parlors were more likely to meet the recommended contact time than tie-stalls; increased number of milkers at milking time and having contact times under 30 s had negative effects on meeting recommended preparation lag time. The majority of farms in the study complied with the recommendations for adequate milking practices; however, most did not follow a consistent timed protocol. There are several potential sources of variation in the milking routine on a dairy farm. To improve milk quality and udder health, it is important to identify whether best management practices are being implemented on each farm. Producers appeared to be consistent in the application of milking procedures across time, regardless of whether or not they were correct. Hence, with corrective education and training, improvements in these practices could be experienced and maintained to promote better udder health.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".