Neural network modeling of carcass measurements to predict beef tenderness
Bibliographic record
Abstract
Neural network (NN) models were developed for predicting and classifying an objective measurement of tenderness using carcass data such as pre-slaughter information (sex, age, kill order), weights, pH, temperatures, lean color readings, lab-determined measurements, grade measurements and organ weights. Tenderness was expressed objectively as Warner-Bratzler shear (WBS) force measured on steaks, aged 6 d, from the longissimus thoracis et lumborum (LTL) muscle. Carcass data from experiments conducted between 1985 and 1995 at the Lacombe Research Centre were combined to form large data sets (n = 775–1177) for modeling. Neural network models to predict actual shear values showed limited potential (R2 = 0.37–0.45) and were only marginally better than a multiple linear regression (MLR) model (R2 = 0.34). Neural network models that classified carcasses into tenderness categories showed better potential (mean accuracy 51–53%). The best four-category (tender, probably tender, probably tough, tough) model classified tender and tough steaks with accuracies of 0.64 and 0.79, respectively. This model reduced tough and probably tough carcasses by 55% in our population. The model required the following 11 inputs, which, except for cooking method, are available by 24 h postmortem: sex, live plant weight, hot carcass weight, 24-h cooler shrink, 24-h pH, 24-h CIE color b*, 24-h CIE lightness L* × hue angle, rib eye area, grader's marbling score (AMSA%), grade, and cooking method. By implementing techniques outlined in this study in a plant situation, the current 23% unacceptable consumer rating for Canadian beef could be reduced to 10–12%. Key words: Neural networks, beef, tenderness, carcass measurements, longissimus muscle
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".