The applications of timed artificial insemination and timed embryo transfer in reproductive management of dairy cattle
Bibliographic record
Abstract
Fertility of dairy cattle is generally decreasing. Overall estrus detection efficiency in lactating dairy cattle is low, as expression of estrus is often compromised. Consequently, undetected estrus, low AI-submission rates, and long inter-breeding intervals are the main contributors to poor reproductive efficiency. Although failure to become pregnant is the most common reason for culling dairy cattle, pregnancy rates could be improved by increasing the AI-submission rate through increased estrus detection efficiency, timed insemination (Timed-AI), or timed embryo transfer (Timed-ET). In these protocols, ovarian follicular development and luteolysis are controlled, culminating in synchronous ovulation in most cows. There are several modifications to improve pregnancy rates in Timed-AI protocols, e.g. presynchronization with 2 doses of PGF2a 14 d apart, and starting the Ovsynch protocol 12 d after the 2nd PGF2a. Timed-AI programs ensure a timely first breeding; this should be followed with early pregnancy diagnosis to identify nonpregnant cows, and prompt re-insemination. However, in a recent field study involving 23 dairy herds, the mean interval between 1st and 2nd breeding was 42 d; only 28% of the cows were rebred within 24 d after 1st breeding. Fortunately, there are several resynchronization protocols to reduce inter-breeding intervals. Anestrus cows, or those with cystic ovarian follicles, have a good chance of conceiving when subjected to synchronization of ovulation and Timed-AI. Furthermore, Timed-ET is a practical and viable option for synchronizing and selecting recipient cattle for embryo transfers. In summary, systematic and judicious use of Timed-AI and Timed-ET protocols can greatly enhance reproductive performance, and improve herd productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".