The importance of B vitamins in enhanced precision nutrition of dairy cows: the case of folates and vitamin B<sub>12</sub>
Bibliographic record
Abstract
Dairy cow diets are generally balanced for energy and major nutrients with B vitamins generally assumed not to be limiting, in spite of their role as coenzymes, essential to many metabolic reactions in protein, carbohydrate, and lipid metabolism. Assuming adequacy of B-vitamin supply may explain some of the discrepancies between the outcomes of metabolic prediction models and measured cow performance. In lactating dairy cow, the amount of B vitamins from the diet and synthesized by the ruminal microbiota is generally sufficient to prevent deficiency symptoms and, as such, is assumed to fulfill requirements. However, reports of beneficial effects of B-vitamin supplementation on dairy cow performance suggest that B-vitamin supply is sometimes lower than its needs, as an insufficient B-vitamin supply decreases metabolic efficiency by driving a shift toward alternative metabolic pathways with greater energy cost. Using information on folates and vitamin B12 illustrated how meeting dairy cow needs for B vitamins should not be overlooked in formulation of rations for lactating dairy cattle. The present review discusses current knowledge and indicates areas presently impeded by the lack of research results, especially the limitations on the ability to estimate B vitamin need and supply.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".