Estimates of variance components and heritability using different animal models for growth, backfat, litter size, and healthy birth ratio in Large White pigs
Bibliographic record
Abstract
This study compared the estimates of variance components using various animal models for Large White pigs. The traits included three production traits, birth weight (BW), age at 100 kg (Age_100), and backfat thickness at 100 kg (BF_100), and two reproduction traits, number of total born (NTB) and the ratio of healthy births (RHB). Five models including or ignoring common litter environmental effects, maternal effects, and the direct-maternal covariance (σam) were used for this study. The results showed that the model including all terms, or including all terms except σam, yielded the best-fitting result. The direct variance and heritability were overestimated when the model ignored all previously listed effects, especially for production traits. When all terms were modeled, the direct heritability estimates ([Formula: see text] ± standard error) were 0.038 ± 0.008, 0.216 ± 0.022, 0.416 ± 0.023, 0.066 ± 0.013, and 0.049 ± 0.007 for BW, Age_100, BF_100, NTB, and RHB, respectively. The common litter effects reached statistical significance for all traits, and maternal heritability reached statistical significance for three production traits. The direct-maternal correlations were negative for all traits but only reached statistical significance for BW. These results indicate that using a more complex model may result in more accurate estimation of variance components in Large White pigs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".