Multiple Factors Affecting Superovulation in Poll Dorset in China
Bibliographic record
Abstract
To expand the breeding flock of Poll Dorset sheep in China, multiple ovulation and embryo transfer breeding program was applied to the limited number of imported Australian Poll Dorset sheep. This study investigated the effects of FSH from three different manufacturers, parity (nulliparous vs multiparous), repeated superovulation, oestrus induction, corpus luteum regression and oestrus delay on Poll Dorset superovulation. The results showed that gonadotropin FSH from Canada Folltropin-V (Ca-FSH) was successfully used for superovulatory treatment with 160 mg-200 mg dosage per ewe and recovered 12.91 ± 7.80 embryos. Multiparous ewes for superovulation treatment were significantly better nulliparous ewes (p<0.05). The successive superovalution treatment reduced significantly embryo collection but did not affect transferable embryo number. Ewes with natural oestrus resulted in significantly higher number of embryos (13.83 ± 4.64) and of transferable embryos (12.00 ± 5.76) than ewes with induced oestrus (7.00 ± 4.92; 4.22 ± 3.42) and unknown oestrus cycle (5.94 ± 3.38; 3.19 ± 2.56, p<0.05). The delayed oestrus ewes at 24 h after superovulatory treatment produced significantly fewer embryos and transferable embryos (0.92 ± 1.51 vs 0.42 ± 0.90) than those with normal oestrus (p<0.01). Furthermore, the more transferable embryos were recovered from ewes with normal corpus luteum than those with corpus luteum regression (5.88 ± 5.09 vs 3.59 ± 4.30 and 8.83 ± 5.75 vs. 6.66 ± 5.41, p<0.01). These results suggest that in our farm practice, a comprehensive treatment method by using the Canadian FSH (Folltropin-V), plus choosing multiparous and natural oestrus ewes with normal corpus luteum might obtain an optimum embryo collection and embryos transfer in sheep.
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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 | 0.000 | 0.000 |
| 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".