Review: Potential Uses of Cloning in Breeding Schemes: Dairy Cattle
Bibliographic record
Abstract
Cloning by nuclear transfer has many potential applications in a dairy cattle breeding program. It can be used to increase the accuracy of selection and therefore the rate of genetic progress, to speed up the dissemination of the genes from animals of exceptionally high genetic merit to the commercial population, and to reproduce transgenic animals. Today, however, the main limitation of the use of cloning besides governmental regulations is its low success rate and consequently the high cost to produce an animal ready for reproduction. As a result cloning is mostly limited to the reproduction of animals of very high genetic merit or that carry genes of specific interest. Examples of this are top-ranked bulls which do not produce enough semen for the demand due to various reasons. A strategy that could be used by artificial insemination (AI) centers would be to create a bank of somatic cells for every bull entering AI facilities long before they are placed on the young sire proving program. The other use of cloning is to assist in the selection and reproduction of bull dams. Marker assisted selection (MAS) can substantially enhance the accuracy of selection for embryos or young animals without comprehensive performance records, and therefore can greatly increase the value of cloning such embryos or young animals.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".