Effects of monensin only, monensin and virginiamycin combination, or monensin and a blend of organic trace minerals and yeast on meat quality of crossbred bulls finished in feedlot individual pens and fed with high-grain diets
Bibliographic record
Abstract
This study assessed carcass characteristics and meat quality of bulls finished in individual pens and fed with different diets. A completely randomized design determined how to feed 24 crossbred bulls (European × Nellore) with four diets over 84 days: CONT) without additives; MONE) inclusion of 30 mg of monensin/kg DM; MO + VI) inclusion of 30 mg of monensin + 30 mg of virginiamycin/kg DM; and MO+AD) inclusion of 30 mg of monensin/kg DM + 1.57 g of a blend of organic trace minerals, live yeast, beta-glucan, and mannans per kg DM (Advantage-Confinamento). MO+VI resulted in lower pH (P < 0.05) and lighter meat (P < 0.05) compared with other treatments. Cooking loss was less (P < 0.05) with MO+AD at 14 days of aging time. At 14 days, Warner-Bratzler shear force was higher for meat from bulls fed with CONT and MONE diets and slower (P < 0.05) for meat from bulls fed with MO+VI and MO+AD diets. In conclusion, including monensin combined with virginiamycin and monensin combined with a blend of organic trace minerals and yeast in the diets of bulls finished in individual pens can improve the color, Warner-Bratzler shear force of meat, and lower cooking losses.
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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) | 0.001 | 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".