Predicting lean meat yield in beef cattle using ultrasonic muscle depth and width measurements
Bibliographic record
Abstract
The value of live ultrasound longissimus dorsi depth and width measurements as predictors of estimated carcass lean meat yield of steers (CARLEAN-S) and bulls (CARLEAN-B) was studied. In trial 1, equations were developed to predict estimated lean meat yield of steers (n = 116) from carcass weight (Eq. 1) or liveweight (Eq. 2), fat depth and l. dorsi area or liveweight, fat depth and l. dorsi depth × width (Eq. 3). Equation 1 was most precise (RSD = 25.6 g kg-1), followed by Eq. 2 (RSD = 27.8g kg-1) and Eq. 3 (RSD = 30.2g kg-1). Equations 2 and 3 predicted CARLEAN-S with similar accuracy (SEP = 23.8 vs. 24.9 g kg-1, respectively) and were highly correlated with each other (r = 0.89) in an independent data set (n = 118). Repeatability and accuracy of pre-slaughter l. dorsi depth and width measurements were studied in yearling bulls (trial 2; n = 191). When ultrasound measurements were expressed as a percentage of the average ultrasound measurement, repeatabilities of l. dorsi depth (SER = 6.2 to 7.8%) and width (SER = 4.2 to 6.1%) measurements were similar to fat depth and l. dorsi area measurements (SER = 17.9 and 4.5%, respectively). When ultrasound measurements were compared to the corresponding carcass measurements, l. dorsi depth (SEP = 10.3 to 13.9%) and width (SEP = 6.7 to 8.5%) measurements were as accurate as fat depth and l. dorsi area measurements (SEP = 32.9 and 8.4%, respectively). Equations were developed to predict CARLEAN-B of yearling bulls (n = 82) from liveweight, 12th rib ultrasound fat depth and either l. dorsi depth × width measurements (Eqs. 4 and 5) or two l. dorsi depth measurements (Eq. 6). All equations had similar precision (RSD = 19.4 to 19.5 g kg-1) and predicted CARLEAN-B similarly (SEP = 25.0, 24.6 and 26.1g kg-1 for Eqs. 4, 5 and 6, respectively) in an independent data set (n = 109). All equations were highly correlated (r ≥0.97) with an equation using ultrasound fat depth and l. dorsi area in the independent data set. Longissimus muscle depth and width measurements were as valuable as l. dorsi area for predicting carcass composition of yearling beef bulls in the present study. Key words: Ultrasound, beef cattle, carcass traits
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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) | 0.001 | 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 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".