Preliminary observations on carcass traits and meat yield of five types of Brahman-influenced grass-fed bulls
Bibliographic record
Abstract
Benefiting from interventions of the savanna ecosystem, breeders in Los Llanos of Apure State (Venezuela) are exploring the opportunity to improve cattle genetics by implementing crossbreeding programs. Fifty yearling bulls of five types of Brahman influence [ST-Brahman (n = 10), F1 Angus x Brahman (F1-Angus; n = 10), F1 Chianina x Brahman (F1-Chianina; n = 10), F1 Romosinuano x Brahman (F1-Romosinuano; n = 10), and F1 Simmental x Brahman (F1-Simmental; n = 10)] were selected to be compared in carcass performance (linear measurements, quality and quantity indicators, Venezuelan and U.S. grades, and cutability) at a desirable conformation endpoint with a suitable market weight of 480 kg. Shorter ST-Brahman carcasses exhibited the most abundant finish, significantly different from the longer F1-Simmental and F1-Romosinuano counterparts. All carcasses fell into the A youngest maturity and were eligible for the USDA "Bullock “class designation; 62% reached the top Venezuelan quality grade, 96% graded US Standard and 64% reached the US yield grade 1, indicating superior cutability. Significant differences in yield of individual cuts (ribeye + strip loin, and cuts from the round) were detected between F1-Romosinuano and St-Brahman, F1-Angus and F1-Chianina counterparts. F1-Chianina bulls had slight but significant advantages in yield of high-valued boneless cuts as compared to those of F1-Romosinuano and F1-Simmental counterparts. Conversely, F1-Romosinuano outperformed F1-Chianina in 1.73 percentage points of medium-valued boneless cuts (P < 0.05). Under the sample selection criteria and harvest endpoint, slight changes in carcass performance can be expected from crossbreeding.
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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 | 0.000 |
| 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) | 0.002 | 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".