A survey comparing meat quality attributes of beef from credence attribute-based production systems
Bibliographic record
Abstract
Markus, S. B., Aalhus, J. L., Janz, J. A. M. and Larsen, I. L. 2011. A survey comparing meat quality attributes of beef from credence attribute-based production systems. Can. J. Anim. Sci. 91: 283–294. Two branded beef programs based on producer-defined production systems differentiated by intangible credence attributes (Organic and Natural) were compared with Commodity beef to determine meat quality and assess consumer acceptability. In each of four slaughter seasons (winter, spring, summer and fall) longissimus lumborum muscle samples were collected from two industry slaughter plants; Organic n=30, 30, 27 and 31; Natural n=30, 27, 29 and 25; Commodity 1 n=12 and 18 for spring and summer, respectively; Commodity 2 n=14 and 12 for spring and fall, respectively. Samples were vacuum packaged and aged for 16±2 d at 2°C. Seasonal effects (P<0.01) were evident for mean shear force, composition, drip loss, colour and pH. While all mean shear values were classified as being tender (<5.6 kg), a smaller proportion of steaks were classified as tender in the Organic beef compared with the Natural and Commodity beef (55.9 vs. 70.3 and 78.6%; P<0.01), indicating that even after industry normal ageing times there was higher tenderness variability in the Organic beef. Fat content (SEM=0.23; P<0.01) was lowest for the Organic line (3.98%) with Natural (5.34%) and Commodity being intermediate (5.73%). Some statistically significant differences (P<0.05) in mean scores for aroma, juiciness, flavour, tenderness and overall acceptability of cooked beef steaks were observed amongst the three production systems when samples were not matched on the basis of intramuscular fat (IMF). Clearly there are measureable differences in quality between “credence” based production systems and commodity beef with an overall better quality in Commodity beef. However, if the consumer is willing to pay for credence-based attributes then there is an opportunity for these production systems to improve the quality of their product, specifically in respect to age at slaughter and content of IMF.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".