Biological Markers of Boar Fertility
Bibliographic record
Abstract
The semen evaluation techniques used in most commercial artificial insemination centers, which includes sperm motility and morphology measurements, provides a very conservative estimate of the relative fertility of individual boars. As well, differences in relative boar fertility are masked by the widespread use of pooled semen for commercial artificial insemination (AI) in many countries. Furthermore, the relatively high sperm numbers used in commercial AI practice usually compensate for reduced fertility, as can be seen in some boars when lower numbers of sperm are used for AI. The increased efficiency of pork production should involve enhanced use of boars with strong reproductive efficiency and the highest genetic merit for important production traits. Given that the current measures of semen quality are not always indicative of fertility and reproductive performance in boars, accurate and predictive genetic and protein markers are still needed. Recently, significant efforts have been made to identify reliable markers that allow for the identification and exclusion of sires with reduced reproductive efficiency. This paper reviews the current status of proteomic and genomic markers of fertility in boars in relation to other livestock species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".