An investigation of feeding high-moisture corn grain with slow-release urea supplementation on lactational performance, energy partitioning, and ruminal fermentation of dairy cows
Bibliographic record
Abstract
The objective of this experiment was to determine if lactational performance and energy partitioning by dairy cows would differ in response to dietary corn grain (CG) types [steam-flaked corn (SFC) vs. high-moisture corn (HMC)] and slow-release urea (SRU) supplementation. Eight multiparous Holstein cows (32 ± 8.2 d in milk) were used in a duplicated 4 × 4 Latin square design with a 2 × 2 factorial arrangement to test four dietary treatments: SFC without SRU, SFC with SRU, HMC without SRU, and HMC with SRU. Supplementation of SRU tended to increase intakes of dry matter (DM) or increased crude protein (CP) intake under SFC, but no effect under HMC, leading to CG × SRU interactions on DM and CP intakes. Neither type of CG nor SRU supplementation affected milk production. The HMC fed at 14.3% DM allowed cows to partition more net energy into body weight (BW) compared with those fed SFC diets, whereas supplementing SRU tended to decrease the portion of net energy partitioned into BW gain under both SFC and HMC diets. These collective results demonstrate that feeding HMC with SRU can be a practical option in high-forage lactation diets to maintain or improve nutrient and energy utilization efficiency.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".