Modification of muscle inherent properties through age at slaughter, growth promotants and breed crosses
Bibliographic record
Abstract
Girard, I., Aalhus, J. L., Basarab, J. A., Larsen, I. L. and Bruce, H. L. 2011. Modification of muscle inherent properties through age at slaughter, growth promotants and breed crosses. Can. J. Anim. Sci. 91: 635–648. A 24 factorial experiment tested the interactions of slaughter age (12–13 or 18–20 mo), growth hormone use, β-adrenergic agonist (β-AA) use and breed cross [Hereford–Aberdeen Angus (HAA) or Charolais–Red Angus (CRA)] on the composition, fibre types, and connective tissue characteristics of m. semitendinosus (ST) and m. gluteus medius (GM) from 112 crossbred steers. Muscle weights increased with slaughter age, implantation and CRA genetics (P<0.05), but were not affected by ractopamine hydrochloride (RAC) (P>0.10).Animal age increased fast glycolytic (FG) and decreased fast oxidative glycolytic (FOG) fibre percentages by 7.2 and 6.6%, respectively, in the ST and increased slow oxidative (SO) and FOG fibre areas in both muscles (P<0.05). Cross-sectional areas of all fibre types were increased in the ST with implantation. In the GM, implantation increased SO (3.1%) and reduced FOG (3.2%) fibre percentages, while RAC reduced the SO (3.8%) and increased the FG (6.1%) fibre percentages (P<0.05).Only GM total collagen content increased with slaughter age (P<0.05),but collagen solubility decreased with slaughter age for both muscles (P<0.05). CRA genetics increased FG percentage in the GM of yearling-fed steers and increased moisture and protein and reduced fat contents of both muscles (P<0.05). In the muscles studied, IMP, slaughter age and animal genetics induced greater changes in muscle inherent properties than RAC.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".