Predicting loin-eye area from ultrasound and grading probe measurements of fat and muscle depths in pork carcasses
Bibliographic record
Abstract
The mathematical relationships between loin-eye area (m. longissimus thoracis) and linear measurements of fat and muscle depth were studied on digitalized images from 250 hog loins cut between the 3rd- and 4th-last ribs. Depth measurements were collected using (1) an Ultrascan 50 ultrasound system on immobilized, live animals, (2) a Hennessy grading probe on hanging carcasses under normal slaughtering conditions and (3) image analysis on digitalized images of chops separated between the 3rd- and 4th-last ribs. Loin-eye area was accurately predicted by its depth when the measurement was performed on digitalized images (R2 > 0.86; RSD < 1.87 cm2). The accuracy of the relationship between loin-eye area and muscle depth was reduced using ultrasound (R2 = 0.58, RSD = 3.29 cm2) or the probe (R2 = 0.29, RSD = 4.28 cm2) due to measurement errors on muscle depth. Muscle flatness, the perimeter irregularity or its angle in relation to the midline did not improve prediction accuracy. Consequently, muscle depth as measured by the Ultrascan 50 ultrasound system should be used with caution to predict loin-eye area since the measurement is only moderately accurate. Measurements obtained with the Hennessy probe under normal slaughtering conditions are not recommended for predicting loin-eye area in pork carcasses. Key words: Pork, backfat, muscle depth, loin-area, ultrasound, prediction
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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 | 0.002 |
| 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".