41 Estimating the heritability of meat quality traits in turkeys
Bibliographic record
Abstract
Abstract Genetic selection for improved meat quality traits has been successfully implemented in many livestock species. The objective of this study was to estimate the heritability of several meat quality traits to assess their selection potential in turkeys. Pedigree toms (n = 1,033) were processed at a commercial facility and live weight, breast meat yield (as a percentage of live weight), ultimate pH, color (CIELAB values), drip loss, cooking loss, and shear force were recorded on M. pectoralis superficialis (fillet). White striping was also rated on a 1–4 scale. Heritabilities were estimated using univariate animal models in ASReml version 4.1. Hatch week and age at slaughter were included as fixed effects in the mode,l and 32 generations of pedigree records were used. Breast meat yield (h2 = 0.62; SE = 0.090) showed the highest heritability and was higher compared to previous estimates, probably due to the smaller sample size. Live weight (h2 = 0.31; SE = 0.078), ultimate pH (h2 = 0.36; SE = 0.087), lightness (h2 = 0.28; SE = 0.086), redness (h2 = 0.22; SE = 0.075), and white striping score (h2 = 0.27; SE = 0.085) all had moderate heritabilities. The estimate for ultimate pH was similar to previous studies in broilers but was high compared to previous studies in turkeys. Estimates for color were similar to those found in previous studies involving turkeys. Drip loss and cooking loss had similar heritability estimates of 0.13 (SE = 0.071) and 0.10 (SE = 0.064), respectively, which were akin to previous estimates in pork. Shear force (h2 = 0.02; SE = 0.056) was found to have a very low heritability. In conclusion, the heritability estimates provided in this study show great potential for the inclusion of meat quality traits in selection programs of turkeys. This study is part of a larger project working towards the implementation of genomic information in the selection of turkeys for improved meat quality.
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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 | 0.002 |
| 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".