Bibliographic record
Abstract
Carcass data from purebred steer (n = 207) and heifer (n = 66) progeny of 15 Charolais sires with carcass expected progeny differences (EPD) were used to quantify the relationship between sire EPD and progeny phenotype for hot carcass weight, fat thickness, muscle area, marbling score, and percent lean yield. The model included six slaughter date × sex subclasses, the linear effect of age at harvest (434 ± 18 d), and the appropriate sire EPD for each trait of interest. Differences of the regression coefficients from their theoretical expectation of one were tested using general linear models procedures. Sire EPD was positively (P < 0.0001) associated with progeny performance for hot carcass weight, fat thickness, muscle area, marbling score, and percent lean yield. For all traits, the regression coefficients were not different (P > 0.27) from one. Regression coefficients were 1.16 ± 0.41 kg, 1.27 ± 0.27 mm, 1.23 ± 0.23 cm2, 1.26 ± 0.23 score, and 0.84 ± 0.19% for hot carcass weight, fat thickness, muscle area, marbling score, and percent lean yield, respectively. These results suggest that carcass EPD for hot carcass weight, fat thickness, muscle area, marbling score, and percent lean yield were related to progeny differences at or near theoretical expectations. Selection for carcass merit using appropriate EPD would be expected to be successful. Key words: Beef cattle, carcass, expected progeny difference
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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 | 0.003 |
| 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 | 0.000 | 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".