Effects of barley and corn as sources of silage and grain on dry matter intake, ruminal fermentation, and total-tract digestibility in growing beef heifers
Bibliographic record
Abstract
The objective of this study was to evaluate the use of barley (BS) or corn (CS) silage when fed with dry-rolled barley grain (BG), corn grain (CG), or an equal blend (BCG) of BG and CG for backgrounding cattle. Ruminally cannulated heifers (n = 5) were assigned to an incomplete 6 × 6 Latin square design. Treatments contained either BS or CS in combination with BG, CG, or BCG. Samples were collected to determine dry matter intake, ruminal fermentation, total-tract nutrient digestibility, and nitrogen (N) balance. Interactions between cereal silage and cereal grain sources were detected for the molar proportions of propionate and butyrate where the magnitude of change with the type of cereal grain was increased in heifers fed CS relative to BS. Feeding CS increased estimated microbial N production (silage, P = 0.022) and fecal N excretion (silage, P = 0.042) over BS. Diets containing BG had greater dry matter, organic matter, starch, and gross energy digestibility values compared with CG, but values for BCG were not different (grain, P ≤ 0.043). Based on limited silage × grain interactions, use of CS in backgrounding diets may increase microbial N production relative to BS and dry-rolled CG may reduce apparent total-tract nutrient digestibility relative to dry-rolled BG.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".