PSI-24 Effects of barley and corn as sources of silage and grain on growth performance, and nutrient utilization for backgrounding steers.
Bibliographic record
Abstract
The objective was to determine the effect of silage and cereal grain source for backgrounding cattle. Steers (288) were stratified by BW into 24 pens and pens were randomly assigned to 1 of 6 treatments (n = 4). Treatments contained either barley silage (BS) or corn silage (CS) included at 55% (DM basis) fed in combination with barley grain (BG), corn grain (CG), or an equal blend of barley and corn grain (BCG) included at 30% (DM basis). Steers were weighed on two consecutive days at the beginning and end of the study, and every 2 wk to determine BW and ADG. Digestibility was predicted using near-infrared spectroscopy using fecal samples. There were no interactions among silage or grain source and no differences in ADG (1 kg/d) or G:F (0.1 kg/kg) among treatments. However, DMI was 0.8 kg/d greater for steers fed corn silage (P = 0.018) than BS. Steers fed CS had greater DM, OM, CP, ADF, starch digestibility and digestible energy content (P 0.01) than those fed BS. Feeding BG improved NDF, ADF, and CP digestibility (P 0.01) over CG or BCG. In. addition, diets with BG had greater starch digestibility than CG with BCG having least starch digestibility (P < 0.01). Fecal starch was greatest for CG, intermediate for BCG, and least for BG (P < 0.01). Whole barley kernels were greatest in BS and BG diets, fragments of barley kernels were greater in BG compared to CG with BCG being intermediate but not different (P < 0.01). Fragments of corn kernels were greatest in CG and BCG (P < 0.01). Relative to barley silage, feeding corn silage improved DMI and nutrient digestibility. Use of dry-rolled BG improved nutrient digestibility and reduced fecal starch content when compared to using CG in diets for backgrounding cattle.
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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".