Effect of forage level and replacing canola meal with dry distillers grains with solubles in precision-fed heifer diets: Digestibility and rumen fermentation
Bibliographic record
Abstract
Objectives of this study were to determine the effects of feeding differing forage-to-concentrate ratios (F:C) and inclusion rates of corn dry distillers grain with solubles (DDGS) on digestion and rumen fermentation in precision-fed dairy heifer rations. A split-plot design with F:C as whole plot and DDGS inclusion level as sub-plot was administered in a 4-period (19 d) 4 × 4 Latin square. Eight rumen-cannulated Holstein heifers (12.5 ± 0.5 mo of age and 344 ± 15 kg of body weight) housed in individual stalls were allocated to 2 F:C [50:50, low forage, or 75:25 high forage; dry matter (DM) basis] and to a sequence of DDGS inclusion (0, 7, 14, and 21%; DM basis). Forage was a mix of 50% corn silage and 50% grass hay (DM basis). Diets were fed to allow for 800 g/d of body weight gain and fed 1×/d. Rumen contents were sampled at -2, 0, 2, 4, 6, 8, 10, 12, and 20 h after feeding for rumen fermentation measures. Low-forage rations had greater DM and organic matter apparent digestibility. We detected a quadratic effect for DM, organic matter, acid detergent fiber, and neutral detergent fiber apparent digestibility, with the 14% DDGS inclusion level having the highest values. Nitrogen retention decreased with increasing levels of DDGS. Molar proportions of acetate tended to be greater for HF and decreased as DDGS increased; propionate increased as DDGS increased, resulting in the opposite effect on acetate to propionate ratio. Rumen protozoa count decreased as DDGS increased. Moderate levels (14% of DM) of DDGS appear to enhance nutrient utilization and fermentation in precision-fed dairy heifers fed different F:C 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".