Accuracy of real-time ultrasound measurements of total tissue, fat, and muscle depths at different measuring sites in lamb1
Bibliographic record
Abstract
Accuracy of live ultrasound measurements to evaluate the total tissue depth (GR), as well as fat and LM depths at different scanning sites, was studied in 96 purebred Suffolk and Dorset lambs of both sexes slaughtered between 36 and 54 kg of BW. Before slaughter, 7 real-time ultrasound measurements were taken on lambs: fat and LM depths between the 12th and 13th ribs (transverse) and between the 3rd and 4th lumbar vertebrae (transverse and longitudinal), and GR. After slaughter, the measurements equivalent to ultrasound measurements were taken on digitized images of the cuts on the left half carcass of each lamb. Ultrasound GR and fat depth measurements were closely correlated with the corresponding carcass measurements (0.76 < or = r < or = 0.81). Ultrasound GR measurement exhibited a large error of central tendency, but the level of error due to the disturbance (ED) was comparable with fat depth measurements (ED = 8.5%; residual SD = 2.24 mm; CV of residuals = 9.5%). Ultrasound fat depth measurements were more accurate between the 12th and 13th ribs (error due to regression = 1.20; ED = 0.82) than between the 3rd and 4th lumbar vertebrae (error due to regression = 5.58 and 5.4; ED = 1.10 and 0.93, transverse and longitudinal, respectively), mainly due to image interpretation errors in the lumbar region. Measurements of LM depth demonstrated low variability in the population under study (SD = 2.6 mm), and these ultrasound measurements showed low correlation with the corresponding carcass measurements (0.34 < or = r < or = 0.43). The results of this study demonstrated that ultrasound measurements were more accurate for evaluating fat depth and the GR measurements than for estimating LM depths. Ultrasound GR measurement is a promising measurement, especially where carcass grading systems are based on this carcass measurement.
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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".