Estimation of direct and maternal genetic parameters for individual birth weight and probe weight using cross-fostering information
Bibliographic record
Abstract
A total of 246 357 measurements of birth (BW) and probe (PW) weights of purebred Yorkshire and Landrace pigs were used to compare the fitting of two alternate models including either common-litter effect or cross-fostering group effect to account for common environmental variation. PW, is a live ultrasonic weight measurement taken when the pigs are 100 ± 30 kg, following national standards. The common-litter effect was defined as piglets born into the same litter, and the group effect was used to account for cross-fostering, and defined as the effect common to piglets raised by the same nurse-sow, regardless of whether that piglet was born into that litter or not. It was found that the cross-fostering group explained 5% more environmental variation in BW when compared with the common-litter effect, indication that BW is a criterion in cross-fostering. Cross-fostering also explained 1% more environmental variation in PW in both the Yorkshire and Landrace. When the cross-fostering group effect was included in place of the common-litter effect, the direct and maternal genetic heritability estimates were similar, but residual variances were reduced. This study advanced the understanding of the effects of cross-fostering on PW, its association with BW and its implications in modern pig breeding programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".