Characterization of Canadian grade standards and lean yield prediction for cows
Bibliographic record
Abstract
Rodas-González, A., Juárez, M., Robertson, W. M., Larsen, I. L. and Aalhus, J. L. 2013. Characterization of Canadian grade standards and lean yield prediction for cows. Can. J. Anim. Sci. 93: 99–107. Commercial carcasses (n=120) were selected to benchmark the current Canadian grading system for cows (D1, D2, D3, D4;>50% ossification) in comparison to A/AA grades youthful carcasses [over (OTM) and under (UTM) 30 mo of age based on dentition but <50% ossification]. With the exception of the D3 and D4 grades, D1 and D2 carcass grades had similar carcass yield attributes compared with OTM and UTM carcasses; however, rib-eye area from UTM carcasses was the largest (P<0.05), followed by D1, D2 and OTM. As expected, both OTM and UTM grades had lower ossification scores (P<0.05); however, D4 grade showed the highest marbling score (P<0.05). For carcass composition, compared with all other grades, the D3 grade had the highest proportion of lean (P<0.05) due to a lower proportion of dissectible fat (P<0.05); however, it had the lightest carcass weight (P<0.05). Using simple measures of carcass characteristics (grade fat, rib-eye area, marbling and ossification) a prediction equation to estimate lean yield (R 2=0.825; Cp=4.31) could be used to more accurately assess carcass value in cows; however, validation of the equation on a separate population would be required before its application.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".