Corn or soybean hull incorporation into haylage-based backgrounding beef cattle diets: Effect on growth and efficiency during the backgrounding and finishing periods
Bibliographic record
Abstract
This experiment was conducted to evaluate the effects of corn or soybean hull incorporation into haylage-based diets on backgrounding calf performance and subsequent feedlot performance. Crossbred steers [n = 48, initial body weight (BW) = 303 ± 3.4 kg] were individually fed dietary treatments consisting of: (1) haylage [17.5% crude protein, dry matter (DM) basis; control], (2) haylage + 20% (DM basis) cracked corn, and (3) haylage + 20% (DM basis) soybean hulls during a 112-d backgrounding period. After the backgrounding period, all steers were adapted to a common high moisture corn-based finishing diet and fed until an ultrasound estimated backfat thickness of 7 mm was obtained. During the backgrounding period, steers fed cracked corn or soybean hulls had greater (P < 0.01) average daily gain, dry matter intake and gain:feed and lower (P = 0.05) plasma urea N as compared with controls. However, when finished on a common high-concentrate diet, steers previously fed cracked corn had greater (P < 0.08) average daily gain, final body weight, and longissimus muscle area than those fed soybean hulls, suggesting that source of supplemental energy during the backgrounding period may influence subsequent feedlot performance. Key words: Beef cattle, backgrounding, energy supplementation
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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) | 0.001 | 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) | 0.001 | 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".