Production factors affecting the contribution of collagen to beef toughness
Bibliographic record
Abstract
Intramuscular collagen may affect the value of meat by limiting its tenderness and cooking convenience. Production factors such as age of animal at slaughter, the use of steroids and beta-adrenergic agonists as growth promotants, and cattle breed may affect the contribution of collagen to beef quality. Recent research has indicated that concentrations of the mature collagen cross-link pyridinoline (PYR) are positively correlated with Warner-Bratzler shear force (WBSF) and animal age at slaughter, while contribution of the concentration of a second mature collagen cross-link Ehrlich's Chromogen (EC) to beef toughness declines with cattle age. Cattle breed influences total collagen content of muscle due to differing rates of maturation among breeds. Growth promoting technologies do not appear to affect collagen solubility, but do influence PYR and EC densities and concentrations in some beef muscles. Concentrations of PYR and EC do not account for all the variation in collagen heat solubility in beef muscles, nor do advanced glycation end products given the relative immaturity of cattle at slaughter. In light of this, other collagen cross-links such as heat-stable divalent cross-links may warrant reconsideration with regard to their contribution to cooked beef toughness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".