Dietary Forage and Nonfiber Carbohydrate Contents Influence B-Vitamin Intake, Duodenal Flow, and Apparent Ruminal Synthesis in Lactating Dairy Cows
Bibliographic record
Abstract
The objective of this experiment was to quantify intakes, duodenal flows, and ruminal apparent synthesis (AS) of B-vitamins in lactating dairy cows fed diets varying in forage and nonfiber carbohydrate (NFC) contents. Eight (4 primiparous and 4 multiparous) ruminally and duodenally cannulated Holstein cows were assigned to 4 dietary treatments in a replicated 21-d period, 4 x 4 Latin square design with a 2 x 2 factorial treatment arrangement. Diets, fed as TMR, contained (DM basis) 2 levels of forage (35 and 60%) and 2 levels of NFC (30 and 40%). The forage portion of the diets contained 50% corn silage, 33% alfalfa hay, and 17% grass hay. Soybean hulls and beet pulp (2:1) and corn meal and ground barley (2:1) were included to achieve desired NFC concentrations. No supplemental B-vitamins were fed. B-vitamin AS was calculated as the amount of a specific B-vitamin flowing to the duodenum minus its daily orts-corrected intake. Dry matter and organic matter intakes were higher for cows fed the 35% forage diets and the 40% NFC diets. Increasing dietary forage content decreased ruminal AS of pyridoxine, folic acid, and B12. Increasing dietary NFC content increased ruminal AS of nicotinic acid, nicotinamide, niacin, pyridoxal, B6, and folic acid but decreased AS of B12. Across diets, amounts of B-vitamins synthesized were highest for niacin, followed by riboflavin, B12, thiamin, B6, and folic acid. Biotin AS values were negative for all diets, suggesting either no ruminal synthesis or that destruction by ruminal microflora was greater than synthesis. B-vitamin intake, duodenal flow, and ruminal synthesis are influenced by dietary forage and NFC contents.
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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".