Quantifying the relative contribution of ante- and post-mortem factors to the variability in beef texture
Bibliographic record
Abstract
This study aims to investigate the relative contribution of ante- and post-mortem factors to the final quality of beef. In all, 112 steers (four breed-crosses) were arranged in a 2 × 2 × 2 factorial experimental including production system, growth implant and β-adrenergic agonist strategies. Carcasses were suspended by the Achilles tendon or the aitch bone and meat was aged for 2/6/13/21/27 days (longissimus muscle) or 2/27 days (semimembranosus muscle). Meat quality traits related to beef texture were measured. Statistical analyses were developed including ante- and post-mortem factors and their relative contribution to the variability observed for each measured trait was calculated. The main factor responsible for the variability in sarcomere length was the suspension method (91.1%), which also influenced drip-loss (44.3%). Increasing the percentage of British breeds increased (P < 0.05) the intramuscular fat content in longissimus muscle, but only when implants were not used. Thus, the breed-cross, implant strategy and their interaction were responsible for >58% of the variability in this trait. The variability in instrumental and sensory tenderness was mainly affected by post-mortem factors (carcass suspension, ageing time and their interaction), explaining generally ∼70% of the variability in these traits. Breed-cross was the second most important effect (∼15%) when carcass suspension was not considered in the model, but still ageing time was responsible for a much larger proportion of the variability in tenderness (>45%). In conclusion, post-mortem handling of the carcasses may be much more effective in controlling beef tenderness than pre-mortem strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".