Influence of milking robot application on cow longevity and amount of somatic cells in milk
Bibliographic record
Abstract
The research has been performed in the teaching and research farm, where a part of cows were milked with two milking robots VMS produced by the company De Laval, but other cows -in a separate parlour with side by side 2x10 type milking equipment.For the research, animals that were not rejected due to traumatism or any specific disease were selected.The information on the length of the productive life of the cows was obtained from the management system of the farm and the Latvian Data Centre In the study, we used data from the Latvian Data Centre (LDC) for 173 Holstein black and white (HM) and 391 Latvian brown (LB) cows.thequality of the obtained milk was evaluated according to the number of somatic cells in it that was determined in the result of laboratory analyses.In the research it was stated that for the cows milked with robots the length of the productive life increases by approximately half year.In turn, evaluating according to the amount of somatic cells, it was stated that the obtained milk complies with the requirements of the normative standards.Nevertheless, it cannot be unequivocally ascertained that using milking robots the quality of milk is always higher than using the side-by-side type milking equipment.
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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 | 0.000 |
| 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".