Different phenotypic and proteomic markers explain variability of beef tenderness across muscles
Bibliographic record
Abstract
This study analyzed the abundance of 24 different proteins, tenderness biomarkers, and 11 phenotypic carcass characteristics and muscle properties. This was done on 111 samples of two muscles, Longissimus thoracis (LT) and Semitendinosus (ST) from the Charolais bovine breed. The strategy was to constitute three classes of tenderness on the two muscles separately on the shear-force data (Warner-Bratzler). Then we tested which proteins or phenotypic characteristics explained this classification. Results showed that tenderness classes of ST muscle were well-explained by 12 proteins and 6 phenotypic characteristics. However, for LT muscle the classification could only be explained by 7 phenotypic characteristics. This demonstrates that in ST and LT the variability of beef tenderness is explained by different factors. In ST muscle, the main results of this study demonstrated the importance of Heat Shock Proteins such as Hsp27 (P = 0.002) and the oxidative stress protein: PRDX6 (P = 0.003). We also confirmed the role of Enolase 3, involved in glycolytic metabolism (P = 0.003), and contractile protein such as MyHC IIx (P = 0.028).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".