Genetic evaluation of retail product percentage in Simmental cattle
Bibliographic record
Abstract
The objective of this study was to estimate genetic parameters required for genetic evaluation of retail product percentage (RPP) in Simmental cattle. Carcass weight (HCW), subcutaneous fat thickness (FAT), longissimus muscle area (REA) and kidney, pelvic, and heart fat (KPH) records were available to compute RPP on steers (n = 5171) and heifers (n = 1400) from the American Simmental Association database; animals were sired by 561 Simmental bulls and out of 5886 crossbred dams. Genetic parameters were estimated using residual maximal likelihood and a four trait animal model for the components of RPP including fixed harvest contemporary group effects, random animal genetic effects, and a linear covariate for age at harvest. Heritability estimates were 0.51 +/- 0.05, 0.36 +/- 0.05, 0.46 +/- 0.05, and 0.18 +/- 0.05 for HCW, FAT, REA and KPH respectively. Non-zero genetic correlations were estimated between HCW and REA (rg = 0.51 +/- 0.06) and between REA and FAT (rg = -0.43 +/- 0.08), but other genetic correlation estimates among the component traits were low. As a linear function of its components, heritability and genetic correlations involving RPP were estimated using index methods. The heritability estimate for RPP was 0.41, and genetic correlations were -0.17, -0.83, 0.67, and 0.01 with HCW, FAT, REA and KPH respectively. Therefore, RPP was strongly associated with muscle and fat deposition, but essentially independent of carcass weight and internal body cavity fat. Genetic evaluation of RPP would be straightforward using multiple trait index methods and genetic regression, although the inclusion of KPH would be of marginal value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".