Efficacy of genetic parameter estimation of pork loin quality of crossbred commercial pigs using technological quality measurements of frozen and unfrozen product
Bibliographic record
Abstract
Meat quality characteristics have been measured on fresh and previously frozen meat as part of genetic studies, but freezing of meat may alter its quality characteristics and, therefore, the relationships between genetic components and meat quality measurements. Pork color, pH, and drip loss measurements performed on longissimus dorsi from the carcasses of 2027 crossbred commercial pigs when either fresh or thawed after frozen storage were used to estimate genetic parameters using a bivariate animal model in ASReml. Meat quality traits measured before and after freezing and thawing were significantly (P < 0.0001) different from each other and intramuscular crude fat content exerted a large effect on the magnitude of change in L* (lightness) and b* (yellowness). Meat quality measurements of fresh pork were moderately to highly heritable except for b* and pH, with heritability estimates for L*, pH, and drip loss greater when measured on fresh rather than frozen-thawed samples. Considering heritability and genetic correlation results, we concluded that whilst either fresh or frozen-thawed pork samples can be used for fresh pork L*, a* (redness), and b* measurements, pH, and possibly drip loss should be measured in fresh pork samples rather than in those that have been frozen-thawed during genetic selection for fresh pork 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 | 0.006 | 0.007 |
| 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 | 0.001 | 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".