Weaning, yearling, and preharvest ultrasound measures of fat andmuscle area in steers, bulls, and heifers1
Bibliographic record
Abstract
Longissimus muscle area and fat thickness were measured following weaning, at yearling, and prior to harvest using real-time ultrasound, and corresponding carcass measurements were recorded 3 to 7 d following the preharvest scan in composite steers (n = 116, 447 +/- 19 d), bulls (n = 224, 521 +/- 11 d), and heifers (n = 257,532 +/- 12 d). Although fat deposition was limited in bulls and heifers from weaning to yearling, coefficients of variation ranged from 8.46 to 13.46% for muscle area, and from 27.55 to 38.95% for fat thickness, indicating that significant phenotypic variance exists across genders. Residual correlations, adjusted for the effects of year of birth, gender, and age at measurement, were high and ranged from 0.79 to 0.87 among ultrasound and carcass measures of muscle area. Residual correlations among ultrasound and carcass measures of fat thickness were also high, ranging from 0.64 to 0.86. Weaning and/or yearling ultrasound muscle area yielded similarly accurate predictions of carcass muscle area. Yearling ultrasound fat thickness accounted for 13% more of the observed variance in carcass fat thickness than the weaning ultrasound measure in single-trait prediction models. When both weaning and yearling ultrasound measures were used to predict carcass fat thickness, partial R2 values were 0.15 and 0.61 for weaning and yearling ultrasound fat thickness, respectively. The difference between predicted and carcass measures with respect to muscle area (fat thickness) was less than 6.45 cm2 (2.5 mm) for 80.2 to 88.9% (90.3 to 95%) of animals. Preharvest ultrasound measures yielded standard errors of prediction of less than 4.95 cm2 for muscle area and 1.51 mm or less for fat thickness. These results indicate that ultrasound measures taken between weaning and yearling provide accurate predictors of corresponding carcass traits in steers, bulls, and heifers.
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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 | 0.000 | 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 | 0.000 | 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".