Frozen Semen Quality of Kalang Buffalo Using Lycopene
Bibliographic record
Abstract
An important factor to successful of artificial insemination (AI) in buffalo was the quality of spermatozoa post-thawed. Therefore, research was conducted on the use of lycopene as an effort to improve the quality of spermatozoa post-thawing. This study aimed to assess the impact of incorporating lycopene into both the skim milk and egg yolk extender at varying doses on pre-freeze and post-thawed Kalang buffalo semen. Four Kalang buffalo, aged between 6 to 7 years and weighing 500-550 kg, were involved in this investigation. Semen collection was conducted weekly a 12-week period using an artificial vagina. Lycopene was added to the skim milk-egg yolk diluent at concentrations of 1%, 2%, 3%, and 4%, while the control group received no lycopene substitution. The freshly collected semen underwent macroscopic and microscopic evaluations. Subsequently, the semen was assessed pre-freezing and post-thaw with parameters such as viability, motility, abnormality, and plasma membrane integrity. The findings revealed that the inclusion of 1% and 2% lycopene in the diluent before freezing exhibited significantly higher (P < 0.05) spermatozoa motility. Moreover, the viability and motility of post-thawed semen significantly improved (P < 0.05) at lycopene doses of 1% and 2%. The addition of 1% to 2% lycopene in the extender demonstrated a positive effect on the sperm qualities, particularly enhancing the viability and motility of post-thaw semen from Kalang buffalo.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".