Effect of an automated dipping and backflushing system on somatic cell counts
Bibliographic record
Abstract
Postmilking teat disinfection is an effective management practice to prevent transmission of contagious mastitis pathogens from cow to cow. With farms increasing in size and an increase in the number of rotary milking parlors, the need for automation of postmilking teat disinfection is mounting. Automated teat dipping and backflushing (ADB) systems have existed for some years, but their effect on udder health was never examined in a field study on commercial dairy farms. The objectives of this study were, therefore, to evaluate the effect of introducing an ADB system in a herd on (1) bulk milk somatic cell count (SCC), (2) individual cow SCC, and (3) the proportion of newly elevated SCC. Dairy herd improvement data were collected over a 30-mo period on 25 sets of 3 farms. Each set of 3 farms contained a farm that installed an ADB system, one that disinfected teats using dipping after milking, and one that sprayed teats after milking. Data were analyzed using linear mixed models. Bulk milk SCC on farms that sprayed or dipped before installing an ADB system were 16,000 and 30,000 cells/mL lower in the period 6 to 18 mo after installation, respectively, than on farms that continued spraying or dipping the teats after milking. In the same period after installing an ADB system, proportions of cows with elevated SCC were 4.3 and 1.2% lower, respectively, compared with spraying and with dipping. Similarly, proportions of cows that had newly elevated SCC were 1.5% lower and 0.3% higher, respectively, compared with farms that sprayed or dipped. Installing an ADB system had a beneficial effect on bulk milk SCC, individual cow SCC, and the proportion of newly elevated SCC. The effect was most prominent in the period 6 to 18 mo after installation of an ADB system.
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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 | 0.001 |
| 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".