The accuracy of measuring backfat and loin muscle thicknesses on pork carcasses by the Hennessy HGP2, Destron PG-100, CGM and ultrasound CVT grading probes
Bibliographic record
Abstract
Research was undertaken to evaluate the accuracy of different grading probes measuring backfat (F) and loin muscle thicknesses (M). Thus, 270 pig carcasses were selected according to a 2 × 3 × 3 factorial arrangement. Gender (barrows and gilts), fat thickness at the Canadian grading site (< 15.75, 15.75 to 19.75 and > 19.75 mm), and hot carcass weight (75.5 to 81.8, 81.9 to 86.2 and 86.3 to 92.7 kg) were the main factors. The Hennessy (HGP2), Destron (PG-100) and CGM optic probes and the CVT ultrasound probe with two transducers [PCA-5049, 172 mm (CVT-1) and PCB-5011, 125 mm (CVT-2)] were evaluated. Grading measures were compared to the equivalent measures taken in a digitized image. The F and M precision was evaluated in terms of random bias (ED). Hennessy F and CVT-1 M had the lower ED. For F measurements, CGM, Destron, CVT-2 and CVT-1 ED was respectively, 1.65, 1.72, 1.78 and 2.14 times greater than Hennessy ED. For M measurements, ED of CVT-2, CGM, DPG and Hennessy was 1.02, 1.84, 2.03 and 2.20 times greater than CVT-1 ED. Measures of the intercostal muscles were not reliable in any of the probes able to take that measure. Key words: Pork, carcass grading, grading probes, HGP2, PG-100, CGM, CVT
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".