Association of single nucleotide polymorphisms in CAPN1 and CAST genes with beef tenderness from Spanish commercial feedlots
Bibliographic record
Abstract
Frequencies of two SNPs in the μ-calpain (CAPN1) and calpastatin (CAST) genes in local and foreign commercial cross-breeds used in south-western Spain (Charolais, Limousin, and Retinta) were evaluated and the association of these markers with texture analysis in animals fattened under different feedlot conditions was assessed. Marker frequencies were estimated in a 286 bull crossbred population and the longisimus dorsi muscles from subsequently selected 161 animals were used to measure Warner-Bratzler shear force in raw and cooked samples at three different ageing days (1, 7, and 21). Significant differences (P ≤ 0.05) were found for shear force in raw and cooked meat samples for the three ageing days for the three crossbreeds analyzed. Significant associations were observed for raw meat for the Charolais between shear force and the CAPN1 marker (P = 0.019), as well as between the CAST polymorphism and shear force (P = 0.027) in the Limousin. No associations were found between the markers and shear force in the Retinta (P > 0.05). In contrast, although these markers might be useful in particular selected populations due to their effect on objective texture parameters, no significant association (P > 0.05) was found for cooked meat in the sample of Spanish commercial crossbreeds used in this study. Further studies with a higher number of animals will be necessary to confirm these results.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".