Effect of variety and level of inclusion of barley varieties for silage selected to vary in neutral detergent fiber digestibility on performance and carcass characteristics of growing and finishing beef steers
Bibliographic record
Abstract
Abstract Three ensiled barley varieties (‘CDC Cowboy’, ‘CDC Copeland’, and ‘Xena’) selected for differences in 30 h neutral detergent fiber digestibility (NDFD30h) were fed at two (LOW and HIGH) inclusion rates to study their effects on performance of crossbred steers (n = 288) in a 3 × 2 factorial design. Diets with the LOW inclusion level during backgrounding had a 1:1 barley silage:barley grain ratio, whereas HIGH diets had a 2:1 ratio (% DM basis). Respective ratios during finishing were 1:17 and 1:5. Actual NDFD30h averaged 37.6% ± 3.5%, 34.7% ± 3.8%, and 36.9% ± 3.0% for ‘CDC Cowboy’, ‘CDC Copeland’, and ‘Xena’, respectively. Backgrounding diets containing ‘CDC Cowboy’ as well as the HIGH diets had greater (P < 0.01) acid detergent fiber (ADF) and neutral detergent fiber (NDF) content. Steers fed ‘CDC Cowboy’ as well as the HIGH diets during backgrounding had lower (P < 0.01) dry matter intake (DMI), average daily gain (ADG), and end of backgrounding body weight. During finishing, ADG and DMI were greater (P < 0.01) for steers fed HIGH barley silage diets. The results indicate that barley variety and inclusion level had the greatest impact during backgrounding and highlight the difficulty in choosing barley varieties for silage based on a single nutritional parameter like NDFD30h.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".