WPSIII-8 The effectiveness of the using bulls evaluated by different methods.
Bibliographic record
Abstract
The aim of the study was to show the efficiency of using bulls (n=18) evaluated by various methods for improvement of herds with average cow milk yield of more than 8000 kg. The material was animal databases. We used statistical data processing. Results of simultaneous calculation of breeding value in bulls, which were genomically evaluated in the USA and Canada, demonstrated that sires evaluated in Canada by their own comparison base exceeded milk breeding value of bulls evaluated in the USA by the national comparison base. Using genomic evaluations and progeny tests in Russian animal breeding one should take into account a difference of bull breeding values in the USA and Canada. Use of semen of the same genomic bulls during period of 2014–2017 demonstrated that among 25 sires with genomic evaluation in the USA one sire maintained its superiority by 95.7% and three sires improved milk yield breeding value by 4.4–35.9%. Among Canadian genomic bulls evaluated in 2017, 6 sires maintained 80.7–97.6% of the breeding value in comparison with that of 2014. Use of genomic bulls demonstrated that best progeny tested sires were also among the best of genomic evaluation. In sire selection one should take into account the superiority by productive traits of daughters of given bulls, change in breeding values which took place during years after 1st genomic scoring. Comparison of results for the same 18 sires genomically evaluated in USA,which were progeny tested in the USA and Leningrad region, resulted in correlation coefficients between EBV BLUP score and “Daughter-contemporaries” score by milk yield about 0.55, by fat content – 0.72, by protein – 0.72. Loss of efficiency of cow breeding for milk yield because of ignoring BLUP method may reach 45%. Thus,the genetic evaluation by BLUP method should be introduce in breeding in dairy cattle.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".