Pregnancy and Embryonic Loss Among Timed Artificial Insemination Protocols in Post-partum Dairy Buffaloes (Bubalus bubalis)
Bibliographic record
Abstract
The aims of the study were to determine the pregnancies and embryonic losses following Timed Artificial Insemination (TAI) protocols and to generate strategic breeding management for buffaloes. In Study 1, buffaloes in natural estrus were inseminated, serving as Control (T1). In T2, buffaloes were subjected to the Controlled Internal Drug Release-Synch-human Chorionic Gonadotropin (CIDR-Synch-hCG) protocol. In T3, animals were subjected to Prostaglandin protocol, and T4 is similar to T3 except that hCG was supplemented on the Day of AI. In Study 2, buffaloes were subjected to the CIDR-Synch-hCG protocol, and the sizes of pre-ovulatory follicles were measured as the basis for the conduct of TAI. In Study 3, animals were similarly subjected to the CIDR-Synch-hCG protocol at different seasons. Results in Study 1 indicate that the CIDR-Synch-hCG protocol achieved pregnancy rates on Days 30 and 60 post-AI and embryonic loss, which are not significantly different (P<0.05) from those of natural estrus (T1), and that the Prostaglandin protocol supplemented with hCG (T4) resulted in significantly higher pregnancies and lower embryonic loss compared with Prostaglandin alone (T3) group. In Study 2, inseminating buffaloes with ≥12.0 mm pre-ovulatory follicle size (T3) yielded the highest pregnancies (P<0.05) at Days 30 and 60 with the lowest (P<0.05) embryonic loss among follicle size groups. In Study 3, TAI performed during January-March and October-December indicated significantly higher pregnancies (P<0.05) with lower embryonic loss (P<0.05) compared with the April-June and July-September seasons. In sum, the present study demonstrated the efficiencies of Timed AI protocols, which provided opportunities for their strategic use in breeding programs to achieve greater productivity and profitability from buffalo farming.
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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 | 0.000 | 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".