Short-Term Effect of Transition from Conventional to Automated Milking on Teat Skin and Teat End Condition
Bibliographic record
Abstract
A higher milking frequency, as a consequence of milking with an automated milking system, incorporates a threat to teat condition. To study the effect of transition from conventional to automated milking on teat skin and teat end condition, 40 lactating Holstein-Friesian cows and heifers from a high yielding dairy herd were randomly allocated to either a conventional or an automated milking system group. In the latter group, automated milking was initiated during the study period, while conventional milking was continued in the control group. Teat skin and teat end condition were evaluated weekly on quarter level for all animals from 5 wk before until 8 wk after transition. A high emollient iodine teat dip was used on all cows during the study period. Teat skin condition of the animals in the automated milking system group was consistent from before and during milking with the automated milking system. Rear teats had a better skin and end condition than front teats. Evolution of teat end condition over time between the automated and conventional milking groups was not statistically different. Heifers, however, seemed to be more sensitive to the change than multiparous cows, as their teat end condition slightly decreased.
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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 | 0.001 |
| 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".