Symposium review: Effects of carbohydrate digestion on feed intake and fuel supply
Bibliographic record
Abstract
Carbohydrates are the primary energy source for lactating dairy cows, and dairy diets are usually formulated for certain concentrations of forage neutral detergent fiber (NDF) and starch due to their direct effects on dry matter intake and milk production. Forage NDF exerts greater filling effects in the rumen than other dietary components and can limit maximum voluntary feed intake of lactating dairy cows. Since an analytical method for NDF was developed more than a half century ago, it has been used widely to characterize forages and diets for dairy cows. However, because NDF is a chemical measurement varying in its digestibility, in vitro digestibility measurements were developed as a biological approach to assess forage quality. Research efforts over the last several decades led to the development of forage cultivars or hybrids with enhanced in vitro NDF digestibility, such as brown midrib, and management practices considering differences in NDF digestibility of forages. In addition, in vitro NDF digestibility and undigested NDF are commonly measured in commercial labs, and estimated rates of digestion are used in dynamic models in an effort to improve the accuracy and precision of diet formulation. Starch digestion in the rumen also varies among starch sources, being affected by grain type, extent of processing, and conservation method. The site and rate of starch digestion affect dry matter intake and nutrient partitioning in dairy cows by modifying temporal supply of fuel. In addition, dietary starch content and its fermentability can affect digestion rates of starch itself and NDF in the rumen. Previous research has increased our understanding of dietary carbohydrates, but its application for diet formulations requires integrated approaches accounting for factors affecting the filling effects of forage NDF, starch digestion, and temporal fuel 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".