Instrumental meat quality characteristics associated with aged <i>m. longissimus thoracis</i> from the four Canadian beef quality grades
Bibliographic record
Abstract
Canadian beef is quality graded to characterize the potential eating quality of the cooked product. Instrumental meat quality characteristics of 48 m. longissimus thoracis (LT, rib eye) from four Canadian beef grades (Canada A, AA, AAA, and Prime, n = 12) before and after an additional 14-d aging were compared using a split plot design with grade, aging, and their interaction as fixed sources of variation. Mean percentage intramuscular fat was greatest in Canada Prime muscle and least in Canada A and AA muscles (P < 0.0001), whereas mean percentage drip loss was lower in Canada Prime muscle than in muscle from all other grades (P = 0.0348). Canada Prime and AAA muscles were redder and yellower than muscles from other grades even after aging (P < 0.03), which may be associated with increased fat content and indicative of accelerated myoglobin oxidation and increased myoglobin oxygenation. Shear force was not different among the Canada grades, although the differences between Canada AA cooked beef LT and that of Canada Prime and AAA carcasses approached significance (P = 0.0993). Results indicated that Canada quality grades did not differentiate beef on cooked product tenderness, substantiating that muscle compositional characteristics alone define beef grade advantages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".