Porcine <i>LIF</i> gene polymorphisms and their association with litter size traits in four pig breeds
Bibliographic record
Abstract
Leukemia inhibitory factor (LIF) is an important productivity-related gene in pigs. We found two polymorphisms — g.6646C>T and g.6988C>T — in exon 3 of LIF in pigs by using DNA sequencing and polymerase chain reaction-restriction fragment length polymorphism. Three genotypes were obtained and associated with litter size traits in Anqing Six-end-white (AQ), Wei (W), Wannan Black (WNB), and Large White (LW) pigs. At locus g.6646C>T, the g.6646C allele frequency variation was 0.6869 (AQ), 0.7473 (W), 1 (WNB), and 0.6852 (LW). In AQ pigs, sows with the TT genotype had higher total number of piglets born (TNB) and number of piglets born alive (NBA) in the first parity and multiparities (P < 0.01). In W and LW pigs, sows with the CC genotype had higher TNB and NBA in multiparities (P < 0.01). At locus g.6988C>T, the g.6988C allele frequency variation was 1 (AQ), 0.6154 (W), 1 (WNB), and 0.6667 (LW). The CC genotype significantly differed from CT or TT genotypes (P < 0.01) for TNB and NBA in W and LW pigs. Thus, LIF was shown to have a significant influence on litter size. Therefore, g.6646C>T and g.6988C>T loci of LIF could be potential marker-assisted selection tools for improving litter size in pig production.
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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 | 0.000 |
| 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".