Genetic evaluation of beef carcass data using different endpoint adjustments
Bibliographic record
Abstract
Carcass data from 6,795 Simmental-sired animals born from 1992 to 2001 were used to determine whether adjustment to a constant age, back-fat, HCW, or marbling score would result in differences in heritability of the carcass traits and, correspondingly, if EPD calculated using those variance components and adjustments would result in sire reranking. The endpoints were age (EPA), backfat (EPF), HCW (EPC), or marbling (EPM). The traits analyzed were 12th-rib backfat (FAT), HCW, marbling (MRB), LM area (LMA), and percentage retail cuts (PRC). The data were analyzed using an animal model, where contemporary group was included as a fixed effect and was composed of slaughter date, sex, and herd. Random effects included in the model were direct genetic and residual. Estimates of heritability ranged from 0.12 to 0.14, 0.32 to 0.34, and 0.26 to 0.27 for FAT, HCW, and LMA, respectively, for the corresponding endpoints. Heritability for MRB was estimated to be 0.27 at all endpoints. For PRC, estimates of heritability were more variable, with estimates of 0.23 +/- 0.05, 0.32 +/- 0.05, 0.21 +/- 0.05, and 0.20 +/- 0.04 for EPA, EPF, EPC, and EPM, respectively. However, because the EPF and EPC adjustments adjust for a component trait of PRC (FAT and HCW, respectively), they may be altering the trait to one different from PRC. Spearman rank correlations between EPD within a trait using EPA compared with the other endpoints were >0.90 (P < 0.01) for FAT, HCW, MRB, and LMA. For PRC, Spearman rank correlations with EPA EPD were 0.73 (P < 0.01), 0.93 (P < 0.01), and 0.95 (P < 0.01) for EPF, EPC, and EPM, respectively. For most traits and endpoints, there was little reranking among sires when alternative endpoints were used. However, adjusting PRC to EPF appears to result in a greater heritability and substantial re-ranking of sires, potentially due to the adjustment changing the trait to one other than PRC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".