Describing the relationships among meat quality traits in domestic turkey (Meleagris gallopavo) populations
Bibliographic record
Abstract
The presence of meat quality defects is increasing in the turkey industry. While the main strategy for mitigating these issues is through improved housing, management, and slaughter conditions, it may be possible to incorporate meat quality into a turkey breeding strategy with the intent to improve meat quality. Before this can occur, it is important to describe the current state of turkey meat quality as well as the correlations among the different meat quality traits and important production traits. The main objective of the present study was to provide a descriptive analysis of 8 different meat quality traits for turkey breast meat from 3 different purebred lines (A, B, and C), and their correlation with a selection of production traits. Using a total of 7,781 images, the breast meat (N = 590–3,892 birds depending on trait) was evaluated at 24 h postmortem for color (L*, a*, b*), pH, and physiochemical characteristics (drip loss, cooking loss, shear force). Descriptive statistics (mean and standard deviation) and Pearson correlations were computed to describe the relationships among traits within each genetic line. A one-factor ANOVA and post hoc t-test were conducted for each trait and between each of the genetic lines. We found significant differences between genetic lines for some color traits (L* and a*), pHinitial, drip loss, and cooking loss. The lightest line in weight (line B) had meat that was the lightest (L*) in color. The heaviest line (line C) had meat that was less red (a*) with a higher pHinitial and greater cooking loss. Unfavorable correlations between production traits and meat quality were also found for each of the genetic lines where increases in production (e.g., body weight, growth rate) resulted in meat that was lighter and redder in color and in some cases (line B and C), with an increased moisture loss. The results of this study provide an important benchmark for turkey meat quality in purebred lines and provide an updated account of the relationships between key production traits and meat quality. Although the magnitude of these correlations is low, their cumulative effect on meat quality can be more significant especially with continued selection pressure on growth and yield.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".