Short Communication: Influence of some meat quality parameters on beef tenderness
Bibliographic record
Abstract
Tullio, R. R., Juárez, M., Larsen, I. L., Basarab, J. A. and Aalhus, J. L. 2014. Short Communication: Influence of some meat quality parameters on beef tenderness. Can. J. Anim. Sci. 94: 455–458. Steaks from longissimus lumborum and semimembranosus muscles, aged 2 or 27 d, were obtained from a population of steers (n=112) managed to produce a range in tenderness (shear force range from 2.57 to 17.2 kg). All available carcass (live weight, hot commercial weight, pH, temperature, marbling, rib-eye area) and meat (objective colour, cook loss, cook time, Warner–Bratzler shear force, myoglobin content, proximate composition and collagen content) quality data were used for the analyses. Multivariate analyses determined which factors influenced tenderness between and within muscles, both before or after ageing. In unaged muscles, soluble collagen explained differences in tenderness among muscles, while factors related to the myofibrillar component explained differences within a muscle. In contrast, in aged muscles, total collagen content was related to tenderness among muscles and the percent soluble collagen content was related to tenderness differences within a muscle.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".