Estimation of genetic and phenotypic parameters for ultrasound and carcass merit traits in crossbred beef cattle
Bibliographic record
Abstract
Miar, Y., Plastow, G. S., Bruce, H. L., Moore, S. S., Durunna, O. N., Nkrumah, J. D. and Wang, Z. 2014. Estimation of genetic and phenotypic parameters for ultrasound and carcass merit traits in crossbred beef cattle. Can. J. Anim. Sci. 94: 273–280. Ultrasound measurements of 852 crossbred steers along with carcass merit measurements on 756 of them were used to examine their genetic and phenotypic parameters. Traits including ultrasound backfat thickness (UBF), ultrasound ribeye area (UREA), ultrasound marbling (UMAR), carcass weight (CWT), carcass grade fat (CGF), carcass average backfat thickness (CABF), carcass ribeye area (CREA), carcass marbling score (CMAR), and carcass lean meat yield (CLMY) were measured on 6 yr of residual feed intake trials from 2003 to 2008. Pairwise bivariate animal models were performed for each combination of traits using ASReml software to estimate heritability, phenotypic and genetic correlations among the traits. Significant fixed effects (contemporary group, and sire breed), covariates (age of dam, slaughter weight, and start test age of animal), and random additive effect were fitted in the models. The heritability estimates for UBF, UREA, UMAR, CWT, CGF, CABF, CREA, CMAR, and CLMY were 0.31, 0.17, 0.37, 0.40, 0.22, 0.25, 0.24, 0.38, and 0.28, respectively. Most of the phenotypic correlations were significant (P<0.05). CWT had low to moderate phenotypic correlations with most of the traits. Results show that heavier CWT tends to have more UREA, CGF, CABF, and CREA. Genetic correlations among these traits varied from weak to strong, but most of them were not significantly different from zero. Greater CREA may lead to decreased UMAR, and UBF due to negative genetic correlations (−0.56±0.32, and −0.45±0.23, respectively). The results support the potential value of ultrasound technology in crossbreed beef cattle breeding programs to generate indicator traits for carcass quality. In addition, carcass lean meat yield correlated favourably with backfat thickness and rib eye area but correlated unfavourably with UMAR. The estimated genetic parameters for ultrasound and carcass merit traits can be incorporated into breeding programs that emphasize carcass quality in Canadian crossbred beef cattle populations.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".