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 <em>Longissimus thoracis/lumborum</em> (<em>LTL</em>) and <em>Gluteus medius</em> (<em>Gm</em>) sampled from cull dairy cows differing in production status (10 lactating vs. 22 dried off) and aging time, were evaluated regarding different rates of pH<sub>2</sub> fall. Shear force related to pH<sub>2</sub> was dependent on muscle and aging time. The intermediate glycolysis led to lower shear force in <em>LTL</em>, while the faster produced best quality in <em>Gm</em>. Purge was influenced by pH<sub>2</sub> (P=0.0077), aging (P&lt;0.0001) and muscle*pH<sub>2</sub> interaction (P&lt;0.0001). Aging affected thawing (P&lt;0.0001), grilling (P=0.0004) and overall losses (P&lt;0.0001). Under the ruled chilling regime, the fast pH fall in <em>Gm</em> and the slow pH fall in <em>LTL</em> 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 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".