Laparoscopic Ovum Pick-Up Followed by In Vitro Embryo Production and Transfer in Assisted Breeding Programs for Ruminants
Bibliographic record
Abstract
The potential of laparoscopic ovum pick-up (LOPU) followed by in vitro embryo production (IVEP) as a tool for accelerated genetic programs in ruminants is reviewed in this article. In sheep and goats, the LOPU-IVEP platform offers the possibility of producing more offspring from elite females, as the procedure is minimally invasive and can be repeated more times and more frequently in the same animals compared with conventional surgical embryo recovery. On average, ~10 and ~14 viable oocytes are recovered by LOPU from sheep and goats, respectively, which results in 3-5 transferable embryos and >50% pregnancy rate after transfer. LOPU-IVEP has also been applied to prepubertal ruminants of 2-6 months of age, including bovine and buffalo calves. In dairy cattle, the technology has gained momentum in the past few years stemming from the development of genetic marker selection that has allowed predicting the production phenotype of dairy females from shortly after birth. In Holstein calves, we obtained an average of ~22 viable oocytes and ~20% transferable blastocyst rate, followed by >50% pregnancy rate after transfer, declaring the platform ready for commercial application. The present and future of this technology are discussed with a focus on improvements and research needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".