Exploration of methods for lamb carcass yield estimation in Canada
Bibliographic record
Abstract
Different approaches were evaluated to improve the accuracy of carcass yield predictions of Canadian lamb carcasses using manually obtained measurements and dual-energy X-ray absorptiometry (DEXA). Several linear carcass measurements were obtained from a population of commercial lamb carcasses representative of the variability in Canadian slaughter plants (n = 155). Carcass measures were categorized into four sets according to when each measure could be obtained in the slaughter process. Each set of carcass measurements were subjected to stepwise regression and used to develop models for the estimation of lean meat and saleable yield percentages. Tissue depth measures (at the GR site) explained 44% of variation in lean meat yield in hot carcasses and 53% in cold carcasses. When additional parameters were included with cold GR, the regression model explained 61.9% of the variability in lean meat yield. Saleable yield predictions were less accurate (R2 < 0.40); the greatest degree of variability was predicted when the model included ribeye area (R2 = 0.39). The DEXA scans obtained on carcass sides were able to predict about 78% of variability in carcass lean meat yield and 91% of fat content. This information could be used by the lamb meat industry to establish new carcass classification systems based on more accurate lean meat yield values.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".