Effects of feeding essential oils and benzoic acid to replace antibiotics on finishing beef cattle growth, carcass characteristics, and sensory attributes
Bibliographic record
Abstract
This study examined the effects of feeding essential oils, benzoic acid, or both, as a replacement for monensin and tylosin, on finishing cattle growth performance, carcass characteristics, meat quality, and sensory properties. Crossbred steers (n = 68; BW = 539 ± 36 kg) were fed 1 of 5 dietary treatments: (1) control (CON; no antibiotics fed); (2) monensin and tylosin (M/T; monensin supplemented at 33 mg/kg on a DM basis; tylosin supplemented at 11 mg/kg on a DM basis); (3) essential oils (EO; supplemented at 1.0 g/steer per day); (4) benzoic acid (BA; supplemented at 0.5% on a DM basis); and (5) combination (COMBO; essential oils supplemented at 1.0 g/steer per day and benzoic acid supplemented at 0.5% on a DM basis). Steers were fed their designated diets for the last 98 d of the finishing period using an Insentec feeding system (Insentec B.V., Marknesse, the Netherlands). A tendency for improved G:F (P = 0.07) was observed; specifically, M/T steers tended to have greater feed efficiency compared with CON, EO, and COMBO steers. Dietary treatment affected (P ≤ 0.05) lipid content in the longissimus thoracis and the following trained sensory attributes: tenderness, chewiness, juiciness, and beef flavor. Specifically, longissimus thoracis steaks from CON steers had less lipid content, whereas longissimus thoracis steaks from CON and COMBO steers were tougher, chewier, and less juicy compared with other treatments. Results from this study suggest that growth performance was similar for steers supplemented with essential oils, benzoic acid, or both versus CON steers, and beef quality from alternative treatments was not compromised.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".