Bayesian analysis of selection for greater weaning weight while maintaining birth weight in beef cattle
Bibliographic record
Abstract
An experimental Hereford herd established in 1960 was used from 1986 to 2006 to select for increased weaning weight (W) without increasing birth weight (B). Data were B and W collected over the 47 yr from 2,124 calves. Including ancestors, the pedigree file had 2,369 animals. Selection was practiced only in males. In the first stage (1986 to 1993), mass-selected bulls were chosen with the index I = B + 9374.76 RDG (relative daily gain). From 1994 to 2006, the selection criterion for bull i was I(i) = BLUP(i)(WD) - 2.33 BLUP(i)(BD), where the BLUP were for the direct BV of B (BD) and W (WD), respectively. Predictions were obtained from a 2-trait animal model with B having only BD, and W with WD and WM (maternal additive effects). Selection response was estimated using a Bayesian approach by means of the Gibbs sampler for a 2-trait animal model including BD, BM (maternal BV for B), WD, and WM. Estimated heritabilities for BD, BM, WD, and WM were 0.40, 0.23, 0.05, and 0.23, respectively. The correlation between BD and BM was close to zero (0.01), and between WD and WM was positive (0.37). The correlation between BD and WD was 0.07, and between BM and WM was 0.58. The 2 methods used to estimate selection response gave similar results. In both periods BD decreased, whereas BM increased. The reduction of BD due to selection was slightly larger in the second period than in the first one. The regression of BV for W increased due to selection in both stages, but selection response was 21.6% larger from 1986 to 1992 than from 1993 to 2006. The maternal effect, WM increased more than 3 times compared with WD in the first period, but ended up being almost the same value as WD in period 2. The Bulmer effect was manifested by the decrease in magnitude of all (co)variance components during selection. It is concluded that selection to increase BW at weaning in beef cattle, although not increasing BW at birth, was moderately effective.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".