PSVIII-10 Genetic parameter estimates of postmortem muscle activity in purebred male turkeys (<i>Meleagris gallopovo</i>)
Bibliographic record
Abstract
Abstract The poultry processing sector has a critical role in fulfilling the global demand for high-quality protein sources and turkey meat is a significant contributor to that market. During poultry slaughter, wing flapping and body movement may occur, primarily driven by the large breast muscles (Pectoralis major and minor). This postmortem muscular activity may have an impact on meat quality attributes such as drip loss, shear force, and breast meat color, and this can influence consumer satisfaction and the marketability of poultry products. The objective of this study was to use an animal model to estimate genetic parameters for postmortem muscle activity in turkeys during slaughter at a commercial poultry processing plant. A dataset including observations from male turkeys (n = 4,899) was collected between June 2018 and November 2019, with a pedigree comprising 25,081 individuals from six purebred genetic lines. An animal model including the fixed effects genetic line, age, hatch week-year, and body weight (BW) was implemented into ASReml 4.2. Postmortem muscle activity was scored by a single observer using a 1 to 4 categorical scale, where score 1 represents none to minimal muscle quivering, score 2 represents mild wing flapping, score 3 represents moderate spasmodic body movement and curled body posture, and score 4 represents severe wing flapping and full body movement. Heritability for postmortem wing flapping activity was low and estimated to be 0.06 ± 0.022. Further research is needed to evaluate the best model for this trait and estimate genetic correlations with other economically important traits. This study represents a first step toward improving meat quality through the investigation of postmortem muscle activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".