PSVIII-36 Late-Breaking Abstract: Somatic cells count in dairy farms of Northern Kazakhstan
Bibliographic record
Abstract
Abstract According to requirements of the Safety of Milk and Dairy Products in our republic somatic cells count should be less than 750,000 cells / mL. Achieving this level is possible with the implementation of somatic cell programs based on the experience of laboratories in Western Europe and Northern America (G.M. Jones). Somatic cells in milk are counted in the United States and Canada as part of the National Dairy Herd Improvement (DHI) program. The result was a significant improvement of the dairy herd by mastitis level (Barkema H.W., Schukken Y.H., Lam T.J., Beiboer M.L., Wilmink H. et al. 1998). Average somatic cell content was less than 200 thousand cells/mL. The purpose was to determine somatic cells count in the herds of Republic of Kazakhstan and to test the SCC program. Research work was carried out under project “Improving the breeding methods efficiency.” The chemical composition and somatic cell count were carried out on a CombiFossFT + infrared analyzer. The results of counting somatic cells in milk of dairy cows in the farms of the northern region, the Republic of Kazakhstan, showed that the quality of milk in most dairy farms meets the requirements of the technical regulation on the quality and safety of milk (table 1). According to the table, it can be said that livestock of dairy cattle by 16% or more are affected by clinical and subclinical mastitis. Moreover, each farm receives less than 6% or more of milk. To increase the efficiency of dairy cattle breeding in the Republic of Kazakhstan, it is necessary to introduce a program for somatic cell counting into the practice of dairy laboratories.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".