Phenotypic and genetic correlations of fatty acid composition in subcutaneous adipose tissue with carcass merit and meat tenderness traits in Canadian beef cattle1
Bibliographic record
Abstract
Bivariate animal models were used to estimate phenotypic and genetic correlations between 9 carcass merit and meat tenderness traits with 25 individual and grouped fatty acids in the subcutaneous adipose tissue of a population of 1,366 Canadian beef cattle finishing heifers and steers. In general, phenotypic correlations were low (<0.25 in magnitude) except for moderate phenotypic correlations of 9-17:1 (-0.29 ± 0.16), 18:0 (0.26 ± 0.14), 11-18:1 (-0.33 ± 0.15), 11-18:1 (0.35 ± 0.14) with Warner-Bratzler shear force measured 3 d postmortem and between 14:0 (-0.36 ± 0.1), 9-14:1 (-0.34 ± 0.08), 9-16:1 (-0.36 ± 0.08), 9-18:1 (0.26 ± 0.07), and sum of branched-chain fatty acids (BCFA; -0.27 ± 0.06) and back fat thickness (BFAT). Genetic correlations were also low for most of the traits. However, moderate to moderately high genetic correlations (0.25 to 0.50 in magnitude) were detected for some traits, including 17:0 (0.4 ± 0.11), 18:0 (0.44 ± 0.12), 9-14:1 (-0.47 ± 0.11), 9-16:1 (-0.43 ± 0.11), and the -6:-3 PUFA ratio (-0.5 ± 0.15) with HCW; 9-14:1 (-0.41 ± 0.13) and 9-16:1 (-0.42 ± 0.13) with BFAT; 17:0 (0.43 ± 0.19) and BCFA (0.45 ± 0.19) with lean meat yield; 13-18:1 (0.40 ± 0.15) with carcass marbling score; sum of CLA (0.45 ± 0.22), 18:2-6 (0.47 ± 0.17), and sum of PUFA (0.48 ± 0.17) with overall tenderness measured 3 d postmortem; the -6:-3 PUFA ratio (0.41 ± 0.22) and sum of CLA (0.42 ± 0.25) with overall tenderness measured 29 d postmortem; and BCFA (0.41 ± 0.27) with Warner-Bratzler shear force measured 29 d postmortem. The genetic correlations observed in this study suggest that contents of some fatty acids in beef tissue and carcass merit and meat tenderness traits are likely influenced by a subset of the same genes in beef cattle. Due to some antagonistic genetic correlations, multiple-trait economic indexes are recommended when fatty acid composition, carcass merit, and meat tenderness traits are included in the breeding objective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".