The quality and yield characteristics of Canada B3 beef carcasses exhibiting medium to good muscling
Bibliographic record
Abstract
Forty-nine Canada B3 carcasses meeting Canada quality grade requirements, but exhibiting medium to good muscling, 12 Canada A and 13 Canada AA carcasses were selected to determine the eating attributes of LL and SM steaks and their saleable meat yield. Canada B3 LL steaks had lower (P = 0.01) shear force value than Canada A and Canada AA LL steaks. For the SM steaks, there was no difference (P > 0.05). The eating attributes of Canada B3 LL steaks (softness, initial tenderness, flavour intensity, chewiness and rate of breakdown) were superior (P < 0.001) to those of Canada A or Canada AA LL steaks. Two attributes (juiciness and amount of perceptible connective tissue) were not different (P > 0.05). Differences in the eating attributes of Canada B3 SM steaks (softness, tenderness and flavour) were also observed (P < 0.05). The saleable meat yield of Canada B3 carcasses was lower (P < 0.05) than that of Canada 1 carcasses, but superior (P < 0.05) to Canada 2 or Canada 3 carcasses. Canada B3 carcasses had a higher (P < 0.05) proportion of front quarter and a lower (P < 0.05) proportion of hindquarter than Canada 1 carcasses. Canada B3 carcasses had lower yields (P < 0.05) of the more valuable cuts and higher yields (P < 0.05) of less valuable cuts, particularly, when compared to Canada 1 carcasses. Key words: Canada beef grade, quality, yield, muscling, eating attributes
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".