Exploring the Effects of Slaughter Weight Class on Belly Quality Attributes of Gilts and Barrows
Bibliographic record
Abstract
This study examined the impact of slaughter weight class (SWC) on a comprehensive set of pork belly quality attributes for 2110 pigs (1055 barrows and 1055 gilts). Pigs were assigned to 2 live weight classes (weight 1: 114.7 kg; weight 2: 128.1 kg), and key carcass and belly traits were assessed. Weight 2 barrows had the greatest (P < .05) carcass fat percentage (34.2%) and intramuscular fat content (4.1%), while weight 1 gilts had the lowest (29.6% and 3.55%, respectively). The predicted lean meat yield was greater (P < .01) in weight 1 pigs (59.7%) and gilts (60.0%) compared with their counterparts. Belly weight was slightly but significantly higher (P < .05) in weight 2 pigs (18.8 kg) and in barrows (18.7 kg) than in weight 1 pigs (18.5 kg) and gilts (18.6 kg). Bellies from barrows showed higher fat percentage than those from gilts (P < .01). Belly length and width were greater (P < .05) in both weight 2 pigs and gilts. Fat-related components (total fat and side: fat, thickness, seam, subcutaneous) were all greater (P < .01) in weight 2 pigs and barrows. Iodine values were greater (P < .01) in weight 1 pigs and gilts, indicating softer fat. Belly bend angle, a firmness indicator, was greater (P < .01) in weight 2 pigs and barrows. These findings highlight the significant influence of SWC and sex on belly composition and firmness, warranting attention as market weights increase, particularly given the trade-offs between firmness and excessive fat deposition for premium belly markets.
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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 | 0.001 |
| 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) | 0.001 | 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".