Feeding a brown midrib corn silage-based diet to growing beef steers improves growth performance and economic returns
Bibliographic record
Abstract
Saunders, C. S., Yang, S. Y., Eun, J.-S., Feuz, D. M. and ZoBell, D. R. 2015. Feeding a brown midrib corn silage-based diet to growing beef steers improves growth performance and economic returns. Can. J. Anim. Sci. 95: 625–631. A feedlot experiment was performed to determine growth performance, ruminal fermentation characteristics, and economic returns for growing beef steers when fed a brown midrib corn silage-based total mixed ration (BMRT) compared with a conventional corn silage-based total mixed ration (CCST). Twenty-four Angus crossbred steers (initial body weight=258±23.2 kg) in individual pens were used in a completely randomized design (n=12). Intake of dry matter was not different between the treatments. Steers fed the BMRT tended to have greater average daily gain (1.54 vs. 1.42 kg d−1; P=0.09) and gain-to-feed ratio (0.165 vs. 0.146; P=0.07) compared with those fed the CCST. Feeding the BMRT increased total volatile fatty acid (VFA) concentration (P=0.01) compared with the CCST, while it decreased molar proportion of acetate (P<0.01), and increased propionate proportion (P=0.01), resulting in decreased acetate-to-propionate ratio compared with the CCST (P<0.01). Steers fed the BMRT increased feed margin (P=0.05) and net return (P=0.02) compared with those fed the CCST throughout the trial. Overall results of this study indicate that feeding the BMRT to growing beef steers enhanced ruminal fermentation and beneficially shifted VFA profiles, which contributed to improved growth and economic performance of steers.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".