Genetic correlations between live yearling bull and steer carcass traits adjusted to different slaughter end points. 1. Carcass lean percentage1,2
Bibliographic record
Abstract
We studied genetic relationships between age-constant live yearling beef bull growth and ultrasound traits and steer carcass traits with dissected steer carcass lean percentage adjusted to slaughter age-, HCW-, fat depth-, and marbling score-constant end points. Three measures of steer carcass lean percentage were used. Blue Tag lean percentage (BTLean) was predicted from HCW, fat depth, and LM area measurements. Ruler lean percentage (RulerLean) was predicted from carcass fat depth and LM depth and width measurements. Dissected lean percentage (DissLean) was based on dissection of the 10-11-12th rib section. Both BTLean (h2 = 0.30 to 0.44) and DissLean (h2 = 0.34 to 0.39) were more heritable than RulerLean (h2 = 0.05 to 0.14) at all end points. Genetic correlations among DissLean and RulerLean (rg = 0.61 to 0.70), DissLean and BTLean (rg = 0.56 to 0.72), and BTLean and RulerLean (rg = 0.59 to 0.90) indicated that these traits were not genetically identical. Adjusting Diss-Lean to different end points changed the magnitude, but generally not the direction, of genetic correlations with indicator traits. Ultrasound scan-age-constant live yearling bull lean percentage estimates were heritable (h2 = 0.26 to 0.42) and genetically correlated with each other (rg = 0.68 to 0.99) but had greater correlations with DissLean at slaughter age (rg = 0.24 to 0.48) and HCW (rg = 0.16 to 0.40) end points than at fat depth (rg = -0.08 to 0.13) and marbling score (rg = 0.02 to 0.11) end points. Scan-age-constant yearling bull ultrasound fat depth also had stronger correlations with DissLean at slaughter age (rg = -0.34) and HCW (rg = -0.25) than at fat depth (rg = -0.02) and marbling score (rg = -0.03) end points. Yearling bull scan-age-constant ultrasound LM area was positively correlated with DissLean at all endpoints (rg = 0.11 to 0.23). Genetic correlations between yearling bull LM method 1 width (rg = 0.38 to 0.56) and method 2 depth (rg = -0.17 to -0.38) measurements with DissLean suggested that LM shape may be a valuable addition to genetic improvement programs for carcass lean percentage at slaughter age, HCW, and fat depth constant end points. At all end points, steer carcass fat depth (rg = -0.60 to -0.64) and LM area (rg = 0.48 to 0.59) had stronger associations with DissLean than did corresponding live yearling bull measurements. Improved methods that combine live ultrasound and carcass traits would be beneficial for evaluating carcass lean percentage at fat depth or marbling score end points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".