Effect of Fermented Winery By-Product Supplemented Rations on the Temperament and Meat Quality of Angus-Hereford X Steers During Feeding in a British Columbia Feedlot
Bibliographic record
Abstract
Novel value-added beef products provide improved product differentiation within the beef marketplace. This study evaluated the impacts of supplementing feeds with fermented winery by-products (WB) to produce the novel beef product labeled by industry as “wine-finished beef”. To evaluate the impact of this feed, a total of 69 Angus-Hereford X steers were placed in four pens (n=18,17,17,17), one replicate pen per treatment, and fed finishing rations containing equal amounts of either 6-7% WB or 6-7% water (Control; C) over a 143 day feeding period. Dietary treatments were not significantly different with respect to energy or chemical contents (P > 0.05). Interestingly, the average daily gains (ADG; kg*d-1) of cattle did not differ between diets (P > 0.05); however, the ADG of all beef cattle changed over the feeding period (P < 0.0041). Cattle temperament, evaluated by measuring their flight speed, changed over time (P = 0.0097), but not between diets (P = 0.6938). Meat quality attributes including chemical, colour, and tenderness properties did not differ (P > 0.05) between diets with the exception of ground steak, which was darker (P = 0.0477) in cattle fed WB compared to C supplemented feeds, respectively. Supplementing cattle feeds with WB provides a new marketing stream for beef products with no observed differences to cattle behaviour, animal gains or meat tenderness.
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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 | 0.001 | 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) | 0.001 | 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".