Different phenotypic and proteomic markers explain variability of beef tenderness across muscles
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it