Alternative Ultrasound Predictors of Beef Carcass Longissimus Muscle Area
Bibliographic record
Abstract
Longissimus width and depth were measured using ultrasound in steers (n = 174), bulls (n = 323), and heifers (n = 347) at yearling and prior to harvest. Yearling and preharvest muscle dimensions and carcass muscle area of bulls were largest (P<0.01). Steers had wider and deeper (P<0.01) longissimus than heifers at yearling; however, preharvest muscle width and depth and carcass muscle area were greater (P<0.01) for heifers. From yearling to harvest, muscle width of bulls and heifers increased at a similar rate, which was greater (P<0.01) than that of steers. Significant (P<0.01) differences existed for muscle depth increase from yearling to harvest, where bulls had the highest deposition rates, heifers had intermediate rates, and steers had the lowest deposition rates. Correlations of carcass muscle area with muscle depth were large and positive (0.52 to 0.81) and slightly larger than correlations with muscle width (0.51 to 0.74). Muscle depth was the best single predictor of carcass muscle area; however, two-trait prediction models including both muscle width and depth were superior to single-trait prediction models. At yearling (preharvest), predicted and carcass muscle areas differed by more than 9.68cm 2 for less than 2% (5%) of steers and heifers and less than 7% (4%) of bulls. Further, yearling and pre-harvest carcass muscle area predictions were within 4.84cm 2 of carcass measurements for approximately 54 to 65% of all animals, respectively. These results indicate that ultrasound muscle width and depth may be alternative predictors of carcass muscle area and may be useful in selection of potential replacements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".