Influence of Ovarian Follicle Sizes and Estrous Signs on Pregnancy Following Progesterone-Based Fixed Time Artificial Insemination in Water Buffaloes
Bibliographic record
Abstract
The objectives of the present study were to elucidate the importance of follicle sizes and estrous signs during Controlled Internal Drug Release-Synch-human Chorionic Gonadotropin (CIDR-Synch-hCG) protocol for Fixed Time Artificial Insemination (FTAI) and to evaluate their association with pregnancy in water buffaloes. Data from riverine buffaloes (n = 207) under the CIDR-Synch-hCG protocol were analyzed. Buffaloes were administered with Gonadotropin-Releasing Hormone (GnRH) with insertion of CIDR on Day 0. Prostaglandin (PGF2α) was given on Day 7 with the removal of CIDR. hCG was given on Day 9, and AI was performed on Day 10. Follicle measurements by ultrasonography were done on Days 0, 7, and 10, and follicle sizes on those days were categorized into I, II, and III. Estrus signs were taken on the day of AI. The pregnancy diagnosis was done on Day 30-35 post-AI. The average size of follicles in Category III is significantly higher than those of Categories I and II, regardless of the Days of the protocol. Pregnancy is significantly (P<0.001) associated with Pre-Ovulatory Follicle (POF) size and uterine tonicity on the Day of AI but not with follicle sizes on Days 0 and 7, nor with mucus discharge discharge (P>0.05). The overall pregnancy rate is 44.44% while performing AI with POF size ≥12.0 mm increased the probability of pregnancy rate to 56.25%. In conclusion, the present study demonstrated a follicle size-based CIDR-Synch-hCG protocol providing new fertility indicators to improve FTAI efficiency in buffaloes with huge application in other livestock species.
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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".