Comparative analysis of various step-dilution techniques on the quality of frozen Limousin bull semen
Bibliographic record
Abstract
BACKGROUND AND AIM: Indonesia has two National Artificial Insemination centers and 17 Regional Artificial Insemination Centers. The frozen semen production techniques differed between the centers, including the type of diluent and semen dilution technique. The aim of the research was to compare the quality of frozen Limousin bull semen diluted using different techniques. MATERIALS AND METHODS: Semen was collected from three sexually mature Limousin bulls using an artificial vagina. Immediately after collection, the semen was evaluated macroscopically and microscopically. Semen that had >70% motile sperm and <20% sperm abnormality was divided into three tubes and diluted with skim milk-egg yolk (SMEY) using three different dilution techniques: One-step dilution (100% SMEY with 8% glycerol) at room temperature ([RT] 20°C until 25°C) two-step dilution (50% SMEY without glycerol at RT, stored at 5°C; and 50% SMEY with 16% glycerol after 1 h stored at 5°C); and three-step dilution (50% SMEY without glycerol at RT, stored at 5°C; and 50% SMEY with 16% glycerol added twice at 1 h and 1.5 h after being stored at 5°C). The diluted semen was loaded into 0.25 mL mini straws, equilibrated, and frozen using a freezing machine. Sperm motility, viability, membranes, DNA integrity, and concentrations of malondialdehyde (MDA) and aspartate aminotransferase (AST) enzymes were evaluated after thawing. RESULTS: 0.05). CONCLUSION: It was concluded that the one-step-dilution technique was better than three-step dilution for cryopreservation of Limousin bull semen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".