Effects of feeding two different blends of essential oils to finishing steers on growth performance, carcass characteristics, meat quality, meat composition, and shelf life
Bibliographic record
Abstract
The objective was to evaluate the effects of feeding two different commercially available blends of essential oils to finishing steers to replace conventional feed additives in feedlot diets. Angus-based crossbred steers [N = 43; starting body weight (BW) = 466 ± 31 kg] were used in a randomized complete block design. Steers were randomly assigned to four different dietary treatments for the 100 d finishing period in which steers were fed high-moisture corn/alfalfa silage/soybean meal diets. Dietary treatments included a negative control (no additives; CON), a positive control (33 mg·kg−1 monensin and 11 mg·kg−1 tylosin added to the diet; M/T), and two different proprietary blends of essential oils [EO-1: 1 g·steer−1·d−1 Victus Liv (DSM Nutritional Products) and EO-2: 4 g·steer−1·d−1 Fortissa Fit 45 (Provimi Canada ULC)]. Growth performance, carcass characteristics, meat quality, sensory attributes (excluding juiciness), most fatty acid parameters, and shelf-life (color and lipid oxidative stability) were generally unaffected by the inclusion of two different commercially available essential oil blends when compared with both a negative control (CON) and a positive control (M/T). This indicates that commercially available essential oil blends may show promise as a replacement to conventional feed additives like antibiotics without causing negative effects to meat quality, storage stability, and eating experience.
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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 | 0.001 | 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".