Bibliographic record
Abstract
Genomic selection is leading to many changes in the artificial insemination (AI) industry in North America, which is now in a transition period. In 2009, 7,894 males and 6,850 Holstein females were genotyped with the Illumina 50K panel, bringing the total number of Holstein animals genotyped to date to 34,323. The short time required to obtain a genomic evaluation on young males more than compensates for their reduced accuracy of evaluation compared to progeny tested bulls, so that theoretically a young bull scheme based on genomic information is more efficient than one based on organized progeny testing. However, several questions remain to answer before AI organizations fully change their breeding strategies. In particular, what will be the producer acceptance of unproven versus proven bulls, how many progeny tested bulls will be required each year to compensate for the loss of prediction accuracy of marker effects over time, and what will be the impact of a decrease in performance recording incentives linked to organized progeny testing on the ability to generate adequate phenotypic data and bull proofs in the future? For the time being, genomic selection has led AI organizations to increase the number of planned matings compared to bulls on the ground, revise contracts with breeders to accommodate the genotyping of progeny from these matings, and collect more embryos from top females. Over all competition has markedly increased for access to these top females. At least one AI organization has been purchasing or leasing females. Top young genotyped bulls are primarily from three well-known proven sires and their sons, which could have a negative impact on the genetic variability of the breed unless new superior sires with different pedigrees are found. Relatively few young bulls were from unproven sires in 2009, but this number will likely increase in 2010. Bulls entering AI may now be used either in progeny testing programs or commercially as unproven bulls. The number of bulls entering AI was similar in 2006, 2007 and 2008. Numbers for 2009 are down for some companies and up for others, but the general trend is a decrease. Data about the relative market share of unproven bulls is not readily available, but individual companies have reported sales ranging from 5% to 40% of their total semen sales. Some of the new genomic tools that will impact the work of dairy cattle breeding organizations in future include low density panels, high density panels and eventually sequencing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".