Short communication: Effect of automatic postmilking teat disinfection and cluster flushing on the milking work routine
Bibliographic record
Abstract
The importance of a consistent and comprehensive milking routine as a critical component of any mastitis control program is well documented. However, as pressure on time increases, farmers are faced with 3 options: (1) adjust the milking routine to suit the time available, (2) undertake the task less thoroughly, or (3) examine which elements of the milking routine can be automated and substitute capital expenditure for labor. A study was undertaken on 5 farms in the United Kingdom in October and November 2007 to assess the effect on milking time of installing a commercial automatic postmilking teat disinfection and cluster back flushing system (ADF). Two of the farms recruited for the study were intending to purchase the ADF system in the near future and 3 farms had already invested in the technology. The farms ranged in size from 120 to 550 cows and included three 90° rapid exit parlors, a herringbone parlor, and an abreast parlor. All 5 farms were visited for 2 successive milkings before the ADF was installed or disabled, and a detailed time and motion analysis was undertaken. After ADF was installed or the system reactivated, a further 2 milkings were monitored. All monitored farms showed a measurable reduction in milking time after the ADF system was installed. However, the magnitude of the reduction was greater than would be expected by simply removing the elements of postmilking teat disinfection and cluster sanitization. The benefits of ADF are greater than simply disinfecting teats and back flushing clusters and the time saving obtained may allow a more structured milking routine that may have additional benefits in terms of mastitis prevention and control.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".