Relationship Between Carcass Weight, Muscle, Fat, and Predicted Lean Yield for Commercial Pigs in Ontario
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Bibliographic record
Abstract
ObjectivesGreater knowledge of variance and relationships of pork carcass parameters could be used to improve performance, efficiency, and profitability of the pork industry. Previous research has investigated the correlation between pork carcass parameters; however, there are still many misunderstandings, particularly in commercially representative pigs. Thus, the purpose of this study was to examine the correlation and variance of carcass weight, fat depth, muscle depth, and predicted lean yield in commercial pigs.Materials and MethodsThe second largest commercial pig slaughter facility in Ontario slaughtered approximately 1.5 million pigs in 2018. Carcass data (hot carcass weight, fat depth, muscle depth, and predicted lean yield) from 1025,572 pigs was used for this study with pigs slaughtered on each production day of 2018 (between January 2, 2018 and December 31, 2018). Hot carcass weight was reported immediately following slaughter as a head-on weight, and fat depth and muscle depth were measured with a Destron PG-100 probe (International Destron Technologies, Markham, Ontario). The equation used for predicted lean yield was the Canadian Lean Yield equation (CLY (%) = 68.1863– (0.7833 × fat depth) + (0.0689 × muscle depth) + (0.0080 × fat depth2) – (0.0002 × muscle depth2) + (0.0006 × fat depth × muscle depth). Pearson product moment correlation coefficients were calculated among all parameters using RStudio version 1.1.456 and R version 3.5.1 statistical software. Correlation coefficients were considered significantly different from 0 at P < 0.05. Correlations were considered weak (in absolute value) for r < 0.35, moderate for 0.36 ≤ r ≤ 0.67, and strong for r ≥ 0.68. Linear regression models were created between parameters that had meaningful relationships using the RStudio statistical software. Gnuplot version 5.2 was used to create scatter plots to allow for better visualization of the correlation between meaningful parameters.ResultsThe mean ± standard deviation for fat depth, muscle depth, hot carcass weight, and predicted lean yield were 18.27 ± 4.12 mm, 65.69 ± 9.06 mm, 105.93 ± 8.39 kg, and 61.03 ± 1.91%, respectively. We observed weak positive correlations between fat depth and hot carcass weight (r = 0.27; P < 0.0001), and between muscle depth and hot carcass weight (r = 0.17; P < 0.0001). We obtained a weak negative correlation between predicted lean yield and hot carcass weight (r = –0.21; P < 0.0001). The predicted lean yield equation used for this set of pigs included measurements for fat depth and muscle depth, so strong correlation between these parameters was expected. We obtained a moderate positive correlation between muscle depth and predicted lean yield (r = 0.39; P < 0.0001) and a strong negative correlation between fat depth and predicted lean yield (r = –0.96; P < 0.0001).ConclusionResults from this dataset revealed that hot carcass weight was generally not correlated with fat depth, muscle depth, or predicted lean yield. The conclusion of this study based on the current dataset is that pigs do not reach a weight threshold where they consistently become fatter or heavier muscled.
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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 it