Minimum Number of Sex-Sorted Frozen Sperm per dose in Sahiwal (Bos indicus) Cattle
Bibliographic record
Abstract
The aim of this study was to determine the minimum number of sex-sorted frozen sperm required for reasonable pregnancies in Sahiwal cow and heifers.Ejaculates from six Sahiwal bulls were processed according to Beltsville sperm sorting technology using a high speed cell sorter and sorted sperm were packaged in 0.25 ml straws with 1.5 million sperm per straw.Non-synchronized Sahiwal heifers (n= 82) and cow (n= 67) were inseminated with unsexed frozen semen (20 million) and sex-sorted frozen semen with 1.5, 3.0 and 4.5 million sperm per dose.Significantly (P < 0.05) lower pregnancy rate was recorded at 1.5 million (25.3%) and 3.0 million (32.5%) sperm compared to unsexed semen (45.4%) and sexed semen of 4.5 million (60%) sperm.No significant difference was found between sexed semen of 4.5 million sperm and unsexed semen.Pregnancy rates of 1.5 and 3 million sexed sperm were 56% and 72% of unsexed semen, respectively.Significantly (P < 0.05) lower pregnancy rates were observed in sorted X-semen (31.32%) and Y-semen (27.30%) compared to unsexed semen (45.40%).Pregnancy rates were significantly (P < 0.05) affected by the sire.The present work indicates that sexed semen as low as 3 million represents an optimal insemination dose and can be used to achieve reasonable pregnancy rates in Sahiwal cattle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".