The effects of gonadotrophin releasing hormone in prostaglandin F<sub>2α</sub>-based timed insemination programs for beef cattle
Bibliographic record
Abstract
Trials were conducted in the spring (May; n = 324) and fall (October; n = 132) with crossbred continental-type beef cows assigned on the basis of parity and postpartum interval to one of three timed-AI treatments and one of two post-AI treatments. The timed-AI treatments were: (DPG) double (14 d apart) PGF2α (Lutalyse®) and AI (day = 0) 72 h after the second PGF2α (day –3); (OVS) Ovsynch® with the second GnRH (Factrel®) at 48 h and AI at 66 h; and (BRC) the same as OVS except that the second GnRH was given at the time of AI. Half of the cows within each treatment were given GnRH on day 14. Plasma progesterone concentrations were determined for the day of the first injection and on days –3, 0, 14, and 21. Timed-AI pregnancy was diagnosed by ultrasonography at day 42 and confirmed at calving. For DPG, OVS and BRC, PGF2α responder rates were 75.9, 51.4 and 71.3%, respectively, in spring (P < 0.05) and 70.4, 70.4 and 59.1% in fall (P > 0.05), and AI pregnancy rates were 28.7, 44.9 and 44.4% in spring (P< 0.05) and 25.0, 40.9 and 43.2% in fall (P > 0.05). Post-AI GnRH had no significant effect on pregnancy or conception rates or day 21 progesterone. The use of GnRH in the PGF2α based timed-AI program improved pregnancy rates and the BRC treatment was as effective as OVS. Neither postpartum interval nor initial progesterone concentration influenced (P >0.05) the effect of GnRH on AI pregnancy rate, and GnRH had no effect (P > 0.05) on twinning rate or gender ratio. Key words: Beef cows, estrous synchronization, pregnancy, timed-AI, progesterone
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".