Meta data analysis of conception rate in relation to sperm motility in Madura superior bulls
Bibliographic record
Abstract
Abstract Madura bulls are Indonesian germplasm with a very high capacity to adapt to dry environments. Madura bulls come from a crossbreed between Zebu (Bos indicus) and banteng (Bos javanicus). One of the breeding strategies of Madura cattle is the use of artificial insemination (AI) with frozen semen. Regarding sperm motility as one of the standard parameters of good semen quality, it is good to know the reliability of sperm motility with the bull fertility rate. This study aimed to determine the conception rate percentage (%CR) relation to sperm motility in Superior Madura bulls. The frozen semen from eight Madura bulls belonging to the National Singosari and Lembang AI centre were used. They were classified based on the selected field reproductive efficiency data from the year 2018 until 2020. Sperm motility was evaluated using Computer Assisted Sperm Analysis (CASA). The data were analyzed using oneway ANOVA and Pearson correlation. The data showed that %CR was significantly higher (P<0.05) and positively correlated with sperm motility. It is proved that sperm motility represents good quality sperm as one of the fertility parameters in Madura bulls.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".