A comparison of carcass characteristics, carcass cutting yields, and meat quality of barrows and gilts
Bibliographic record
Abstract
Abstract Objectives of this research were to compare carcass characteristics, carcass cutting yields, and meat quality for market barrows and market gilts. Commercially-sourced carcasses from 168 market barrows and 175 market gilts weighing an average of 107.44 ± 7.37 kg were selected from 17 different slaughter groups representing approximately 3,950 carcasses. Each group was sorted into percentiles based on hot carcass weight with an equal number of barrows and gilts selected from each quartile so that weight minimally confounded parameters of interest. Carcass lean yield was determined for carcasses following fabrication (i.e. dissection of lean, fat, and bone tissue components) and meat quality measurements were evaluated at the time of fabrication (24 to 72 h postmortem) and following 14-d of postmortem storage. Data were analyzed as a randomized complete block design with carcass serving as the experimental unit, sex (barrow or gilt), the three hot carcass weight quantiles (light [<104 kg]; average [104 to 110 kg]; heavy [>110 kg]), and the interaction between sex and hot carcass weight quantile serving as fixed effects, and producer nested within slaughter event serving as a random effect. Results from the study demonstrated that gilt carcasses were leaner (3 mm less backfat thickness; 3.5 cm2 greater loin muscle area, 1.52% greater merchandized-cut yield, and 2.92% greater dissected carcass lean yield; P < 0.01) than barrow carcasses, while loins from barrows were higher quality (0.43% more intramuscular fat and slightly less shear force; P < 0.01) than loins from gilts. While this study confirms the well-known biological principle that barrow carcasses have greater levels of fat deposition and lower levels of carcass leanness when compared with gilt carcasses, this study provides a much-needed quantification of these differences for the commercial industry that will undoubtedly be useful as new technologies emerge in upcoming years.
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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 | 0.001 | 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) | 0.002 | 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".