The evolution of improved and simplified superovulation protocols in cattle
Bibliographic record
Abstract
Superovulation protocols have improved greatly since the early days of bovine embryo transfer when purified gonadotrophins were not available, follicular wave dynamics were unknown physiological phenomena and prostaglandins were not available. Although superstimulatory protocols in cattle are normally initiated mid-cycle, elective control of follicular wave emergence and ovulation have had a great impact on the application of on-farm embryo transfer. However, the most common treatment for the synchronisation of follicular wave emergence involves the use of oestradiol, which cannot be used in many parts of the world. Therefore, the need for alternative treatments has driven recent research. An approach that has shown promise is to initiate follicle-stimulating hormone (FSH) treatments at the time of the emergence of the new follicular wave following ovulation induced by gonadotrophin-releasing hormone. Alternatively, it has been shown that it may be possible to ignore follicular wave status and, by extending the treatment protocol, induce subordinate follicles to superovulate. Finally, the short half-life of pituitary FSH necessitates twice-daily treatments, which are time-consuming, stressful and subject to error. Recent treatment protocols have permitted superstimulation with a single FSH treatment or two treatments 48h apart, reducing the need for animal handling during gonadotrophin treatments.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".