Non-surgical embryo transfer in goats and sheep: the Brazilian experience
Bibliographic record
Abstract
Brazil has presented tremendous progress in non-surgical embryo transfer (NSET) in sheep and goats. New instruments and techniques for non-surgical embryo recovery (NSER) and NSET in small ruminants were implemented. Recent improvements include refinement of the protocols for cervical relaxation combining oestradiol-oxytocin-cloprostenol treatment at specific times before NSER in sheep; recipient goats do not require any hormonal drugs to induce cervical dilation and direct embryo transfer by the cervical route yields excellent results. Transrectal ovarian ultrasonography (B-mode but especially colour Doppler) have proven to be accurate methods to localise and enumerate corpora lutea and luteinised unovulated follicles in recipient and donor does and ewes. An array of new criteria for selecting superior animals for NSER and NSET (e.g. cervical mapping) have been developed by Brazilian researchers. Extensive studies on both technologies were initially conducted in commercial breeds of goats and sheep but have been gradually extended to some native breeds of sheep (germplasm conservation) and dairy goat operations. It is speculated that, in future, NSER and NSET may become methods of choice for caprine and ovine embryo recovery and transfer in Brazil, and then globally. Due primarily to the efficiency of NSET in goats, a novel interspecies (e.g. bovine) IVP method may soon be developed on a large scale. The Brazilian experience is an invaluable source of information and know-how promoting the replacement of conventional surgical assisted reproductive technologies with non-surgical procedures and hence supporting the rapid development of the embryo transfer industry in small ruminants.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".