Effect of timing of artificial insemination in relation to onset of standing estrus on pregnancy per AI in Nili-Ravi buffalo
Bibliographic record
Abstract
The aim of the present study was to determine the optimum time of artificial insemination after the beginning of standing estrus in buffalo. Nili-Ravi buffalo (n = 109) during breeding season were exposed to teaser bull at 12 hours interval to determine the standing heat (0 h). Buffalo were randomly allocated to different time groups and a single artificial insemination was performed either at 0 h (n = 30), 12 h (n = 27), 24 h (n = 28) or 36 h (n = 24). In a subset of buffalo (n = 38) ultrasonography was performed, twice daily from 0 h (onset of standing heat) to determine the time of ovulation. Pregnancy diagnosis was performed 35-40 days after AI. Results revealed that mean time of ovulation from onset of standing heat was 34.7 ± 0.96 h (range 30 to 42 h). Higher (P < 0.05) pregnancy per AI were achieved in buffalo when inseminated at 24 h (15/28; 53%) compared to 0 h (8/30; 26%) and 36 h (3/24; 13%). Pregnancy per AI, was in-between, in buffalo, inseminated at 12 h (10/27; 37%) and did not differ (P > 0.05) with those bred either at 24 h or 0 h. The odds ratio further confirmed that the occurrence of pregnancy per AI was two times higher in buffalo inseminated at 24 h as compared to those at 12 h. It is concluded that optimal pregnancy per AI can be achieved when buffalo are bred artificially 24 h after the onset of standing heat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".