Evolution of knowledge on ovarian physiology and its contribution to the widespread application of reproductive biotechnologies in South American cattle
Bibliographic record
Abstract
As our understanding of ovarian function in cattle has improved, our ability to control it has also increased.Luteal function in cattle has been studied in detail, and prostaglandin F2α has been used for several years for the elective induction of luteal regression.More recently, follicle wave dynamics has been studied and protocols designed to induce follicular wave emergence and ovulation have reduced, and even eliminated, the need for estrus detection.The addition of progestin-releasing devices, estradiol, GnRH and equine chorionic gonadotropin (eCG) have provided opportunities for fixed-time AI (FTAI) and possibilities for increased pregnancy rates.In embryo transfer programs, these same treatments have eliminated the need for estrus detection, permitting fixed-time embryo transfer and the initiation of superstimulatory treatments without regard to day of the estrous cycle.Collectively, new treatment protocols have facilitated the application of assisted reproductive technologies, and this is especially true in South America.Over the last 20 years, the use of AI in South America has increased, due largely to the use of FTAI.There has been more than a 10-fold increase in the use of FTAI in Brazil with more than 11 million treatments in 2016, representing 85% of all AI.Similar trends are occurring in Argentina and Uruguay.Production of in vivo-derived (IVD) embryos has remained relatively stable over the years, but in vitro embryo production (IVP) has increased dramatically over the past 10 to 15 years, especially in Brazil where more than 300,000 IVP embryos were produced in 2010.World-wide, more than 666,000 bovine IVP embryos were produced in 2016, of which more than 57% were produced in South America.The use of assisted reproductive technologies has facilitated the dissemination of improved genetics and increased reproductive performance; other South American countries are now following suit.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".