50 Genomic selection in the dairy industry: excitement, challenges, and future directions
Bibliographic record
Abstract
Abstract Dairy cattle, and Holsteins in particular, were the first major agricultural industry to fully embrace genomic selection (GS). Few of us had predicted that, by May 2019, the number of genotyped animals would exceed 3 million. Farmers are changing the frequency of individual alleles and making use of genomic information to select better bulls; identify elite embryo donors; determine if a cow should be bred with conventional, sexed or beef semen; become an embryo recipient or be culled. Our industry has traditionally been an open system, where top genetics are sourced from the general population and phenotypes are provided on a voluntary basis. Contractual agreements have allowed this system to continue and have been extended to access of data, differential pricing, international collaboration and more. Phenotypes no longer come from well designed, highly organized progeny testing programs but rather from paid contributors who provide data that are quality certified and representative of the population. Over time, we’ve improved our genome map, SNP chips, reference populations, statistical models, computing ability, data pipeline, and most importantly our knowledge of genetics. Combining different types of data from multiple sources continues to be a challenge. Interdisciplinary approach and collaboration with other scientists are now the norm. GS has caused a paradigm shift within the dairy industry. But, after 10 years, we are in a better position to more quickly integrate new scientific knowledge. Making our industry and the production of dairy products more efficient and sustainable.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".