Effects of Physically Effective Fiber on Digestion and Milk Production by Dairy Cows Fed Diets Based on Corn Silage
Bibliographic record
Abstract
Effects of physically effective (pe) neutral detergent fiber (NDF) content of dairy cow diets on nutrient intakes, site and extent of digestion, microbial protein synthesis and milk production were evaluated in a double 3 x 3 Latin square design using 6 lactating dairy cows with ruminal and duodenal cannulas. During each of 3 periods, cows were offered 1 of 3 diets that were chemically similar but varied in peNDF content (high, medium, and low) by altering corn silage particle length. The peNDF contents were determined using the Penn State Particle Separator and were 11.5, 10.3, and 8.9%, for the high, medium, and low diets, respectively, and the physical effectiveness factors for the long, medium, and fine silages were 84.1, 72.6, and 67.2%, respectively. Increased forage particle length increased intake of peNDF but did not affect intakes of nutrients including dry matter, NDF, starch, and nitrogen. Except for starch, apparent digestibilities of nutrients in the total tract were linearly increased with increasing dietary peNDF. Fiber digestion was affected by dietary peNDF to a greater extent than were the other nutrients. However, increased digestibility due to increased dietary peNDF did not significantly improve milk production or milk composition. Increased dietary peNDF also increased numerically rumen microbial protein synthesis due to increased amount of organic matter fermented in the rumen. These results indicate that increasing the peNDF content of a corn silage based diet improves digestibility, especially digestibility of fiber, in the total tract. Dietary particle size, expressed as peNDF, is positively associated with nutrient digestibility when level of peNDF in the diet is low.
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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) | 0.000 | 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".