Replacing Chopped Alfalfa Hay with Alfalfa Silage in Barley Grain and Alfalfa-Based Total Mixed Rations for Lactating Dairy Cows
Bibliographic record
Abstract
The effects of replacing chopped alfalfa hay with alfalfa silage in a fine barley grain and alfalfa-based total mixed ration (TMR) were evaluated. Diets contained (dry matter basis) 53.0% commercial energy supplement, 10.3% commercial protein supplement, and 9.7% corn silage. Diets varied in inclusion of chopped alfalfa hay and alfalfa silage, and contained either 20.0% chopped alfalfa hay and 7.0% alfalfa silage, 10.0% chopped alfalfa hay and 17.0% alfalfa silage, or 27.0% alfalfa silage. Contents of crude protein, neutral detergent fiber (NDF), acid detergent fiber, and minerals did not differ among diets. Replacing chopped alfalfa hay with alfalfa silage decreased dietary dry matter, and increased dietary soluble protein and physical effective NDF calculated as the proportion of dietary NDF retained by the 8- and 19-mm screens of the Penn State Particle Separator (peNDF(NDF)) from 13.3 to 15.6% DM. Replacing chopped alfalfa hay with alfalfa silage did not affect dry matter intake, rumen pH, rumen volatile fatty acids, blood lactate, milk fat, and milk protein percentage, but did decrease blood glucose, tended to increase blood urea, and numerically decreased milk yield and milk protein yield. A wider range in peNDF(NDF) and a higher inclusion of corn silage might have resulted in greater differences in rumen fermentation and milk production among diets. The pH of rumen fluid samples collected 4 h after feeding varied from 5.90 to 5.98, and milk fat percentage varied from 2.50 to 2.60% among diets. These values suggest that mild subacute ruminal acidosis was induced by all diets.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".