PSVII-3 Effect of feeding finishing steers two different commercially available blends of essential oils with or without benzoic acid on growth performance, carcass characteristics, and meat quality
Bibliographic record
Abstract
Abstract This study examined the effects of feeding two different commercially available blends of essential oils with or without benzoic acid on growth performance, carcass characteristics, and meat quality of finishing steers. Angus-based crossbred steers (N = 76; allocation BW = 429 ± 30 kg; starting BW = 466 kg ± 31 kg) were assigned by allocation weight into two blocks. Within each block, steers were randomly assigned to one of seven dietary treatments for a 100 d finishing period. Treatments were: 1) a negative control with no additives; 2) a positive control with supplementation of monensin/tylosin; 3) essential oil blend #1 (Victus Liv, DSM Nutritional Products); 4) essential oil blend #2 (Fortissa Fit 45, Provimi Canada); 5) benzoic acid (VevoVitall, DSM Nutritional Products); 6) a combination of essential oil blend #1 and benzoic acid; and 7) a combination of essential oil blend #2 and benzoic acid. All feed additives were supplemented at dosage levels according to manufacturer instructions. Growth performance, carcass characteristics, and meat quality were evaluated. Individual animal feed intake was collected using an Insentec feeding system, therefore steer was the experimental unit for all analyses. Data were analyzed using a RCBD with fixed effect of treatment and random effect of block. Final BW, ADG, DMI, and G:F were similar (P > 0.25) among treatments. There were no treatment differences (P > 0.15) for the carcass characteristics or the meat quality parameters evaluated in this study. Overall, steers supplemented with the commercial blends of essential oils with or without benzoic acid had similar growth performance, carcass characteristics, and meat quality parameters as steers fed CON or M/T, indicating that these products may have potential as replacements for monensin and/or tylosin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".