Genetic correlations among indicator traits for carcass composition measured in yearling beef bulls and finished feedlot steers
Bibliographic record
Abstract
Genetic correlations were examined among 10 live growth and ultrasound traits measured in yearling beef bulls (n = 2172) and four carcass traits measured in crossbred finished feedlot steers (n = 1031). Heritabilities ranged from 0.13 (bull ultrasound longissimus muscle width) to 0.83 (yearling bull hip height). Genetic correlations indicated that selecting yearling bulls for increased growth rate and hip height would lead to higher carcass weight, increased longissimus muscle area and reduced levels of carcass marbling in steers. Bull ultrasound fat depth was positively associated with both carcass fat depth and marbling score. Most ultrasound longissimus muscle size measurements in bulls were positively associated with each other and with carcass longissimus muscle area in steers, but the magnitude of the genetic correlation with carcass measurements depended on the bull longissimus muscle size trait in question. This suggests that longissimus muscle shape in bulls may be related to carcass weight, fat and muscle traits in steers. Results confirm that while ultrasound is a valuable tool for the genetic improvement of carcass traits in beef cattle, genetic correlations between live bull ultrasound and steer carcass traits less then unity suggest that selection would benefit from multiple trait evaluations in situations where reliable carcass data are available. Key words: Ultrasound, beef carcass, heritability, genetic correlation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".