Factors affecting embryo production in superovulated Bos taurus cattle
Bibliographic record
Abstract
Despite a long history of bovine superovulation research, significant commercial applications did not start until the early 1970s. For some 20 years thereafter, superovulation represented the primary tool for the production of cattle embryos. In the early 1990s, commercial invitro production (IVP) was initiated in cattle. Although ovum pick-up and IVP are now commercially practiced on a wide scale, superovulation and embryo recovery by flushing remain a widespread and very effective approach to the production of cattle embryos. This review covers both the history and the effects of multiple factors on superovulation in Bos taurus cattle. There are three general protocols for suitable pre-FSH programming of donors so that gonadotrophin-responsive follicles are available. Superovulation protocols vary widely based on the FSH source, the diluent used, the number and timing of FSH injections and the timing and utilisation of various prostaglandins, controlled internal progesterone releasing devices, gonadotrophin-releasing hormone, and other means of controlling follicular development and ovulation. The number of oocytes that can be stimulated to grow and ovulate within any given donor can be estimated by either ultrasound-guided sonography or by measuring concentrations of anti-Müllerian hormone in the blood. Animal-related factors that can influence the efficacy of superovulation include cattle breed, age, parity, genetics, lactational status and reproductive history. In addition, nutrition, stress, season, climate, weather and several semen factors are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".