Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation
Bibliographic record
Abstract
Artificial insemination (AI) of dairy cattle in Canada was started more than half a century ago and today it is estimated that at least 75% of all dairy cattle nationally are bred using this common reproductive technology. A Best Linear Unbiased Prediction sire model for estimating genetic evaluations for production traits was introduced in 1975. The combination of extensive use of AI with genetic evaluations for bulls and cows has resulted in significant phenotypic and genetic gains over the past 20 yr. In the Holstein breed, mature equivalent yields have increased by an average of 200 kg milk, 7.0 kg fat and 6.3 kg protein per year since 1980. Genetically, the relative emphasis realized for production traits versus overall type during the past 5 yr has followed the 60:40 breeding goal represented in the Lifetime Profit Index, which has increased at an average rate of 0.28 standard units per year. Examination of the generation interval in the Canadian Holstein breed, associated with each of the four pathways for genetic improvement, indicates a 46% increase in the rate of annual genetic gain today compared to 20 yr ago. The increased accuracy and intensity of selection associated with the use of AI and genetic evaluations have also contributed to the rates of phenotypic and genetic progress achieved over the years. In the future , AI will continue to be a critical component of the genetic gains possible in dairy cattle breeding but it will be complemented by other reproductive technologies aimed at further reducing generation intervals and increasing the accuracy and selection of intensity, especially on the female side. Key words: Dairy cattle, artificial insemination, genetic progress, genetic evaluation
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".