Reduced age at slaughter in youthful beef cattle: Effects on carcass merit traits
Bibliographic record
Abstract
López-Campos, Ó., Basarab, J. A., Baron, V. S., Aalhus, J. L. and Juárez, M. 2012. Reduced age at slaughter in youthful beef cattle: Effects on carcass merit traits. Can. J. Anim. Sci. 92: 449-463. Two-hundred and twenty-four spring-born British×Continental crossbred steers were used in a 2-yr project to evaluate the effect of production system (calf-fed vs. yearling-fed) and its interaction with breed cross and hormone implant strategies, with and without β-adrenergic agonist on carcass characteristics. Carcasses from yearling-fed steers were 32% heavier (P<0.001), resulting in higher (P<0.05) dressing percentages, grade fat and rib-eye (longissimus thoracis) area (REA) (1.1, 32 and 10%, respectively). However, despite being lighter, the estimated lean yield percentage was 3% greater (P=0.010) in carcasses from calf-fed steers. No difference (P>0.05) was observed for marbling scores between production systems. Use of hormonal implants increased (P<0.001) weights of live animals and carcasses (7 and 9%, respectively). However, non-implanted yearling-fed steers had the lowest proportion of Canada Quality Grade A and the highest proportion of Canada Quality Grade AAA carcasses (P<0.001). Moreover, the observed increase (P=0.016) in marbling scores (up to 37%) from British cross-bred steers disappeared with the use of implants. The only effect observed on carcass traits from the use of β-adrenergic agonists was an increase of 6% in REA (P=0.032). The main production system effect observed for carcass composition was a lower (P=0.008) proportion of bone in carcasses from yearling-fed steers. The use of hormonal implants increased (P<0.001) the proportion of lean and decreased (P=0.019) the proportion of fat (P<0.05). Overall carcass composition of steers with large Continental influence (>50%) had a higher proportion of lean and bone and a lower proportion of fat than carcasses from 50-75% British steers (P<0.001), which was also reflected in the composition of several individual primal cuts (e.g., rib, short-loin, flank, chuck and plate). The interactions amongst production systems and the other production factors studied were minimal. Therefore, despite expected differences in carcass size, reducing age at slaughter did not have a negative impact on Canadian beef carcass traits.
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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 | 0.000 | 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) | 0.001 | 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".