Machine milking and daily changes of cow’s teat condition
Bibliographic record
Abstract
The paper deals with daily changes of teat parameters of dairy cows caused by machine milking. Ultrasonographic scanner GE Medical Systems LOGIQ 100 PRO with linear array VE 5 – 5 MHz probe was used for scanning the teats. The scanning was conducted on a dairy farm in Križevci on 27 cows, nineteen of them of Holstein breed and eight of Simmental breed or SimmentalxHolstein crossed breed. The cows were housed in a free stall barn and milked in a herringbone 2x3 milking parlour with Alfa-Laval milking equipment with Duovac milking units. Teat scanning was done just before morning milking and immediately after milking on the right side of the udders for front and rear teats. A total of 432 measurements were carried out during the experiment. The following parameters were measured: teat canal length (TCL), teat end width (TEW), teat cistern width (TCW) and teat wall thickness (TWT). As fifth parameter ratio between teat wall thickness and teat cistern width (TWT/TCW) before and after milking was calculated. Length of teat canal for the front right teat increased in average for 15.44% after milking, and 24.38% for the rear right teat. Teat end width of the front and rear right teat increased after milking for 3.7% and 3.32%, respectively. Mean teat cistern width of the front right teat decreased after milking for 24.41% while for the rear right teat mean decrease was 25.83%. Teat wall thickness of the front and rear right teat increased after milking for 7.86% and 15.81% respectively. The ratio between teat cistern width and teat wall thickness and teat cistern width changed for the right front teat from 0.647 before milking to 0.924 after milking, and for the right rear teat from 0.649 to 1.013. Testing of significance of differences of teat dimensions before and after machine milking showed significant differences for all teat parameters, with the exception of teat end width for front right teat (P = 0,069).
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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 | 0.001 | 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".