The effects of modified carcass chilling on beef carcass grade and quality of several muscles
Bibliographic record
Abstract
To determine the effect of modified carcass chilling on beef carcass grade and meat quality, paired sides were assigned to modified (5°C for 24 h then 0–2°C until 48 h post-mortem) or control chilling (0–2°C for 24 h). After grading at the completion of respective chilling treatments, the longissimus lumborum (LL), longissimus thoracis (LT), semimembranosus (SM), semitendinosus (ST), and infraspinatus (IS) were removed and evaluated immediately or aged for 7, 15, 21, or 29 d prior to evaluation. Estimated cutability was not affected by chill treatment. Modified chilled sides tended (P = 0.15) to have greater marbling scores than control with ~65% having scores 10–110 units greater than control. In ~15% of paired sides, this difference was sufficient to result in upgrading from Canada AA to Canada AAA. Modified chilling reduced mean shear value across all muscles, with these early effects persisting throughout ageing in the LL and LT and represented a savings of at least 7 d of refrigerated ageing time. Neither modified chilling nor ageing could be relied upon to produce consistently tender meat in the SM and ST. Prior to ageing the IS was the most tender muscle and underwent gradual but significant tenderization during ageing. Since tenderness is the most important meat quality trait, industry adoption of cut specific ageing, combined with modified carcass chilling, would appear to be beneficial to ensure consistent and high-quality beef. Key words: Marbling, ageing, tenderness, beef carcass chilling
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".