Understanding the underlying genetics of primal lean meat yield traits in Canadian commercial beef
Bibliographic record
Abstract
Multiple investigations were undertaken to evaluate the effects of different factors on primal lean meat yield traits including: i) the effects of production factors, such as calf-fed and yearling-fed production systems (PS), the use of implant strategies, and their interactions along with breed composition, on individual beef primal tissue composition (i.e., lean, fat, and bone); (ii) estimation of phenotypic correlations and genetic parameters, such as heritability and genetic correlations of primal tissue component traits, i.e., lean and fat tissue; and (iii) identification of relevant genomic regions and candidate genes associated with total carcass and individual primal lean content in the Canadian commercial crossbred beef cattle population. The results showed that PS × implant (IMP) interaction affected marbling and primal weights but had no significant effect on the lean or fat content of any primal or total carcass. Redundancy analyses (RDA) demonstrated that lean content from loin was not closely associated with other primals, which could result in a potential effect on the value of grading carcasses using just one primal, which is influenced differently compared with the other primals. The heritability estimates for primal lean and fat traits were also greater than those of primal cut weights. Similarly, genetic correlations were high among the tissue groups while high negative correlations were observed between lean and fat components, implying that lean meat yield (LMY) could be optimized by selecting for one or more tissue component traits without increasing overall carcass fatness. Moreover, actual lean meat yield (ALY) from carcass cut-out data correlated more strongly with primals other than loin, which is the commercially used yield prediction criteria. In addition to that, GWAS indicated that ELY had a large number of genes unrelated to carcass traits while ALY was linked to more genes associated with lean tissue production. Hence, inclusion of primal data into breeding programs can help identify cattle with improved LMY.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".