Impact of monensin, virginiamycin, trace minerals, and yeast combination on animal performance, carcass and meat characteristics of finishing feedlot bulls fed high grain diet
Bibliographic record
Abstract
Abstract A combination of monensin, virginiamycin and minerals trace could have a synergistic effect on animal performance and meat characteristics of bulls finished in feedlot. This study was realized to evaluate the influence of monensin, virginiamycin, minerals trace and yeast combination on animal performance, feed efficiency, in situ digestibility, feeding behavior, and carcass and meat characteristics from bulls finished in feedlot fed high-grain diet. A total of 36 (European vs. Nellore) bulls at 24 ± 3.2 months of age and with a body weight (BW) of 385.5 ± 3.84 kg were used in a completely randomized design. The four experimental diets were: CONT – without additives; MONE – inclusion of 30 mg of monensin/kg of DM; MOVI – inclusion of 30 mg of monensin + 30 mg of virginiamycin/kg of DM; MOMY – inclusion of 30 mg of monensin/kg of DM + 3.0 g of mineral traces and yeast/100 kg of BW. Final body weight was higher (P < 0.05) for the bulls fed MOVI (558.7 kg) and MOMY (554.6 kg) diets, intermediate for bulls fed MONE (529.3 kg) diet, and lower for bulls fed CONT (514.6 kg) diet. Likewise, the average daily gain was higher (P < 0.01) for bulls fed MOVI and MOMY (2.02 kg/d), intermediate for bulls fed MONE (1.72 kg/d), diet and lower for bulls fed CONT (1.57 kg/d) diets. Dry matter intake and other nutrients were similar (P> 0.05) among treatments. However, feed conversion improved (P < 0.05) for bulls fed MOVI and MOMY diets. Animals fed MOVI and MOMY spent more time ruminating compared to animals fed CONT and MONE (P = 0.034) diets. The carcass weights, carcass dressing and backfat thickness were greater (P< 0.05) for the bulls fed MOVI and MOMY diets. In conclusion, the inclusion of monensin or a combination of monensin + virginiamycin or monensin + mineral trace and yeast resulted in higher animal performance and improved feed efficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".