PSVIII-41 Late-Breaking Abstract: Milk urea level of dairy cows in Northern Kazakhstan
Bibliographic record
Abstract
Abstract The high milk productivity of cows with an inadequate feeding level is the cause of many animal diseases. To control protein and energy in feeding ration it may be used as an indicator the milk urea content (Nousiainen, J.K.J. Shingfield, and P. Huhtanen, 2004). The norm of its content is in the range of 15–30 mg% (Smith, J., G. Verkerk, B. McKay, 2000). The purpose of the work was to introduce milk urea indicator in Republic of Kazakhstan by the experience of USA and Canada milk labs. Research work was carried out under project “Improving the breeding methods efficiency.” The studies were carried out using infrared analyzer CombiFoss FT +. The results of the study are shown in table 1. As you can see, milk urea content in Agrofirm Rodina LLP was 34.25 ± 0.29 mg%. Analysis of cows diet in this farm showed, there it was protein excess by 7.9% in comparison with the norms. In the second farm, Esil-Agro LLP, it was a different case. Milk urea content was 11.7 mg%. Low level of urea in this case was the result of energy and protein lack in the diet of dairy cows. It can be concluded that in conditions of dairy farms in the Republic of Kazakhstan, milk urea can serve as reliable indicator of protein and energy level in the diets of dairy cows, monitoring its content will ensure the rational use of expensive protein feeds, preserving animal health and thereby increase the efficiency of milk production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".