Evaluation of Total Lean and Saleable Meat Yield Prediction Equations and Dual Energy X-Ray Absorptiometry for a Rapid, Non-Invasive Yield Prediction in Beef
Bibliographic record
Abstract
ObjectivesThe objective of this study was to evaluate the beef yield equations currently used in North America and the potential use of the Dual energy X-ray absorptiometry (DXA) technology to predict either total or saleable yield of beef carcasses. Materials and MethodsA total of 316 left carcass sides over a wide range of weight (192 to 453 kg) and backfat thickness (1 to 29 mm) were fabricated into primal and retail cuts. Carcass break points were identified following Institutional Meat Purchase Specifications (IMPS) for Fresh Beef Products, Series 100. The primals collected from the left fabricated carcass side were the chuck (IMPS #113), rib (IMPS #103), brisket (IMPS #118), flank (IMPS #193, non-trimmed), foreshank (IMPS #117), loin (IMPS #172A), round (IMPS #158A) and plate (IMPS #121) primal cuts. All the cuts were scanned with an iDXA unit (GE Lunar Prodigy Advance, General Electric, Madison, WI) and then for the chuck, rib, loin and round, broken into closely trimmed retail cuts. Cuts were then fully dissected into fat [subcutaneous (SQ), intermuscular (IM) and body cavity (BC)], lean and bone and weighed. ResultsRegressing total lean meat yield predicted using the Canadian grade ruler versus dissected total lean meat yield resulted in an R² of 0.56 (i.e., the equation predicted 56% of the variation). Regressing USDA calculated meat yield estimation (saleable yield) versus actual saleable yield of the boneless, closely trimmed round, loin, rib and chuck retail cuts resulted in an R² of 0.34. Regressing total lean meat yield versus saleable meat yield yielded a moderate R² (0.63). DXA was able to accurately predict total lean and total fat content in the carcass (R² = 0.98) using partial least squares regression (PLSR). Predictions of saleable yield for each of the four major primals, using DXA technology were slightly lower (R² ranged from 0.70 to 0.87) than those for total carcass lean and fat estimations. ConclusionAccurate prediction of beef yield is required to provide fair settlements for producers, and to help guide genetic improvements. This database provides important knowledge regarding the prediction accuracy and relationships between total lean meat yield and saleable meat yield necessary to support North American grade harmonization. In addition, DEXA technology may have the potential to estimate beef carcass traits such as total or saleable yield performance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".