Canadian beef tenderness survey: 2001–2011
Bibliographic record
Abstract
Juárez, M., Larsen, I. L., Klassen, M. and Aalhus, J. L. 2013. Canadian beef tenderness survey: 2001–2011. Can. J. Anim. Sci. 93: 89–97. A large survey across Canada was developed collecting retail beef samples in 2001 (702 steaks) and 2011 (602 steaks). The samples (strip loin, top sirloin, inside round and cross-rib steaks) were evaluated for instrumental tenderness using standard procedures for sampling, storage, cooking and texture evaluation. New equations were also developed in order to compare the results obtained in these studies with consumer thresholds developed in Canada and the United States of America. In general, retail steaks collected in 2011 weighed less and showed higher fat thickness than those from 2001. Regarding tenderness, a significant improvement was observed, especially for strip loin and top sirloin steaks between 2001 and 2011. Using US threshold categories, the percentage of “tender” samples improved for the strip loin (2001=89%; 2011=99%), top sirloin (2001=70%; 2011=87%), inside round (2001=52%; 2011=61%) and cross-rib (2001=65%; 2011=76%) steaks. Similarly, the percentage of “tough” samples shifted from 5, 8 27 and 13% for the strip loin, top sirloin, inside round and cross-rib steaks in 2001 to 1, 5, 13, and 8%, respectively, in 2011. Similar improvements were observed when using the more descriptive four-category Canadian threshold system. These improvements may be due to changes in the animal population, production systems, carcass processing and distribution/handling prior to display in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".