Use of barley silage or corn silage with dry-rolled barley, corn, or a blend of barley and corn on predicted nutrient total tract digestibility and growth performance of backgrounding steers
Bibliographic record
Abstract
The objective of this study was to determine the effects of feeding barley silage (BS) or corn silage (CS) with dry-rolled barley (BG), dry-rolled corn (CG), or a blend of barley and corn grain (BCG), on growth performance and nutrient digestibility, the latter predicted using near-infrared spectroscopy for backgrounding cattle. Steers (n = 288) were stratified by body weight (BW) into 24 pens and pens were randomly assigned to one of six treatments (n = 4) in a 2 × 3 factorial design. Main factors included BS or CS in combination with BG, CG, or BCG. There were no silage × grain interactions. Dry matter intake (DMI; P = 0.018) and final BW (P = 0.004) were greater for steers fed CS than BS, but average daily gain (1.01 kg d−1) and gain-to-feed ratio (0.10 kg kg−1) were not affected by silage or grain source. Steers fed CS also had greater dry matter, organic matter, crude protein (CP), acid detergent fiber (ADF), starch, and gross energy digestibility values (P < 0.01) than BS. Feeding BG increased starch, neutral detergent fiber, ADF, and CP digestibility values (P ≤ 0.01) over CG and BCG. Relative to BS, feeding CS increased DMI, final BW, and nutrient digestibility, whereas dry-rolled BG improved nutrient digestibility when compared with CG and BCG in backgrounding diets.
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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 | 0.000 | 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".