Effects of skeletal separation method and postmortem ageing on carcass traits and shear force in cull cow beef
Bibliographic record
Abstract
Sixty-six cull cow carcasses were subjected to skeletal separation methods for improving beef tenderness as evaluated using shear force. Forty-one carcasses were used to evaluate the effect on longissimus muscle shear force from skeletal separation at various sites including: (1) the 11th thoracic vertebra, (2) the 12th thoracic vertebra, and (3) the 6th thoracic and 5th lumbar vertebrae. Longissimus muscle steaks from the posterior and anterior loin and posterior rib were aged for 2, 7, 14, and 28 d. Twenty-five carcasses underwent skeletal separation processing in the round with severing of the ischium and the junction between 4th/5th sacral vertebrae. Semimembranosus, semitendinosus, biceps femoris, vastus lateralis, and rectus femoris muscles were processed into steaks and aged for 2, 7, 14, and 28 d. Skeletal separation techniques involving thoracic and lumbar vertebrae decreased (P < 0.04) shear force in the posterior rib and anterior loin but not in the posterior loin. Shear force continued to decrease (P < 0.01) as postmortem ageing duration increased. The semimembranosus was the only muscle in the round in which shear force decreased (P < 0.01) with skeletal separation. Postmortem ageing for at least 14 d decreased (P < 0.06) shear force in semimembranosus and vastus lateralis steaks while there were no further decreases in shear force after 7 d ageing for semitendinosus and rectus femoris steaks. Skeletal separation increased (P < 0.10) sarcomere length in all muscles studied. Skeletal separation techniques have the potential to improve tenderness in the longissimus and semimembranosus muscles, which may increase utilization of cull cow beef. Key words: Beef, cow, shear, tendercut, longissimus, round
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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) | 0.000 | 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".