Challenges and opportunities for global dairy cattle breeding - A Canadian perspective
Bibliographic record
Abstract
Around 6,000 Holstein bulls are newly proven each year worldwide, and 80% of those newly proven bulls are sampled in 9 countries. The major semen exporters are from US, Canada and the Netherlands, but other countries have been increasing their share of the global market. In 2008, Canada has exported $71M of dairy semen. The advent of genomic selection will provide new opportunities and challenges in the global dairy semen market. The market will partly shift from proven sires to young genotyped bulls, provided one can confirm over the next few months that the genetic level and accuracy of evaluation of these young bulls are as high as expected. There is a global race to be among the first to offer this new ‘product’. However, it becomes a priority to be cautious and take all the steps necessary to offer on the market something that is consistent and reliable. Because of the large number of tested bulls, genomics will be first applied in the Holstein breed. The other dairy breeds, like Jersey, Ayrshire and Brown Swiss will run the risk to fall behind, if genomic selection is attempted only at the national level. Global cooperation among countries for those breed may be the best alternative to test and adopt this new technology. Another might be the use of SNP haplotypes to mark a large number of QTL, an approach that would require a smaller “training set” of bulls than genomic selection per se. Finally, genome wide selection may make it easier for multinational AI organizations to offer different groups of young bulls for different selection objectives corresponding to various local markets.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 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".