Male involvement in fertility and factors affecting semen quality in bulls
Bibliographic record
Abstract
Fertility varies substantially among bulls. In general, methods to predict fertility are better for identifying bulls with low fertility than for ranking bulls with good to excellent fertility. Compensable sperm abnormalities can be overcome by increasing the dose used for artificial insemination; these are attributed to sperm reaching and penetrating the zona pellucida. In contrast, increasing the insemination dose does not improve fertility for uncompensable defects, implying that the sperm are able to cause fertilization and initiate development, but they do not sustain embryogenesis. Bull testes must be 2 to 6°C cooler than core body temperature for fertile sperm; consequently, increased testicular temperature reduces semen quality. Increased nutrition before 30 weeks of age increased luteinizing hormone pulse frequency, hastened puberty, and increased testicular size at maturity in bulls. However, attempts to correct nutritional deficiencies present during calfhood by supplemental nutrition later in life were unsuccessful.
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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) | 0.002 | 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".