Effects of Early Post-Mortem Rate of pH fall and aging on Tenderness and Water Holding Capacity of Meat from Cull Dairy Holstein-Friesian Cows
Bibliographic record
Abstract
Fast or slow muscle pH fall may give unacceptable purge losses or tough meat, depending much on concomitant evolution of muscle temperature early post-mortem, costing millions of euros to the meat industry. Tenderness and purge losses of Longissimus thoracis/lumborum (LTL) and Gluteus medius (Gm) sampled from cull dairy cows differing in production status (10 lactating vs. 22 dried off) and aging time, were evaluated regarding different rates of pH2 fall. Shear force related to pH2 was dependent on muscle and aging time. The intermediate glycolysis led to lower shear force in LTL, while the faster produced best quality in Gm. Purge was influenced by pH2 (P=0.0077), aging (P<0.0001) and muscle*pH2 interaction (P<0.0001). Aging affected thawing (P<0.0001), grilling (P=0.0004) and overall losses (P<0.0001). Under the ruled chilling regime, the fast pH fall in Gm and the slow pH fall in LTL approached out of the ideal pH6/temperature limits, being compatible with heat and cold shortening, respectively.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".