PSIV-A-13 Estimation of Nellore Genetic Contribution Over Certified Angus Beef Sold in Brazil
Bibliographic record
Abstract
Abstract Meat traceability and certification increase consumers’ confidence in making purchasing decisions. In Brazil, certified Angus beef follows specific regulation, which requires a minimum of 50% of Angus genome in its breed composition to be commercialized as certified Angus beef. Because the majority of the beef produced in Brazil is Nellore-based, the objective of this study was to estimate the Nellore genetic contribution over certified Angus beef sold in Brazil using genetic traceability. Two hundred sixty-one certified Angus ribeye steaks were obtained from four different brands in commercial establishments. DNA extraction and genotyping were performed for each sample. Genotype data from purebred Angus and Nellore were also available, generating three datasets: meat samples, Angus, and Nellore. Samples and single nucleotide polymorphism (SNP) markers with call rate lower than 0.90 and duplicated samples were excluded from the analysis for each group. Thus, 75,242 SNPs and 99 samples were unique and remained in the meat sample group. For the genotyped reference groups, Angus had 34,586 SNPs and 71 samples while Nellore had 743,665 SNPs and 1,931 samples. The software Admixture were used for the genetic composition prediction using only SNPs in common across the three groups (29563 SNPs). On average, meat samples presented 57% and 43% of Angus and Nellore genome composition, respectively, with proportions ranging from 28% to 87% of Angus genome. Additionally, a principal components analysis showed a clear separation between Nellore and Angus population, whereas the evaluated meat samples were clustered between the two groups. In conclusion, certified Angus beef sold in Brazil have a great contribution of Nellore and some samples had lower Angus contribution than the expected by the certification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".