A Method for Accuracy of Genetic Evaluation by Utilization of Canadian Genetic Evaluation Information to Improve Heilongjiang Holstein Herds
Bibliographic record
Abstract
The objectives of this study were to set up a new genetic evaluation procedure to predict the breeding values of Holstein herds in Heilongjiang Province of China for milk and fat production by utilizing Canadian pedigree and genetic evaluation information and to compare the breeding values of the sires from different countries. The data used for evaluating young sires for the Chinese Holstein population consisted of records selected from 21 herds in Heilongjiang Province. The first lactation records of 2 496 daughters collected in 1989 and 2000 were analyzed. A single-trait animal model including a fixed herd-year effect, random animal and residual effects was used by utilizing Canadian pedigree and genetic evaluation information of 5 126 sires released from the Canadian Dairy Network in August 2000. The BLUP procedure was used to evaluate all cattle in this study and the Estimated Breeding Values (EBV)for milk and fat production of 6 697 cattle (including 673 sires and 6 024 cows) were predicted. The genetic levels of the top 100 sires originated from different countries were compared. Unlike the BLUP procedure that is being used in conjunction with the single-trait sire model in Heilongjiang Province of China now, the genetic evaluation procedure used in this study not only can be used simultaneously to evaluate sires and cows but also increase the accuracy of evaluation due to using the relationships and genetic values of the Canadian evaluated sires with more daughters. The results showed that the new procedure was useful for genetic evaluation of dairy herds and the comparison of the breeding values of these sires imported from different countries showed that a significant genetic improvement has been achieved for milk production of the Heilongjiang Holstein dairy population by importing sires from foreign countries, especially from the United States due to the higher breeding values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".