Factors Affecting Superovulation and Embryo Transfer in Boer Goats
Bibliographic record
Abstract
Despite many studies, results of superovulation protocols are not consistent in farm animals.In this study, 151 Boer goats were superovulated to examine the factors affecting superovulation and embryo transfer (MOET).An optimal regime for superovulation treatment was identified as a 4-day treatment with decreasing dosages of 6-7 mg Chinese FSH or 240 mg Canadian FSH.The 4-day treatment with decreasing dosages of 6-7 mg Chinese FSH was, therefore, adopted to study effects of the age of does, season and repeated treatments on superovulation and embryo transfer.The best season for superovulation and embryo transfer and pregnancy was autumn, and the best age range was 12-35 months old.Within animals there were no significant differences in the number of ovulations and the rate of transferable embryos between the first and the second superovulation.However, these parameters declined significantly for the third superovulation.No marked effects of the number of ovulations on the proportion of transferable embryos were noted.The parturition rate of the recipients receiving single embryos was not different significantly from those receiving two embryos, and the kidding rate calculated from embryos transferred did not differ significantly between recipients receiving one and two embryos.(Asian-Aust.J. Anim.Sci.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".