Short communication: Effects of molasses supplementation on performance of lactating cows fed high-alfalfa silage diets
Bibliographic record
Abstract
Twelve Holstein cows were used in a replicated Latin square experiment to determine the effect of adding dried molasses to high-alfalfa silage diets on dairy cow performance. Three isonitrogenous diets were formulated with a 68:32 forage:concentrate ratio, with alfalfa silage as the only forage source. Dietary treatments were a control diet with no added molasses and 3 and 6% dried molasses diets. Three lactating Holstein cows fitted with ruminal cannulas were used to determine the effects of dietary treatments on ruminal fermentation. Dietary treatments had no effect on dry matter (average 23.3 kg/d), crude protein (average 4.4 kg/d), or neutral detergent fiber (average 7.4 kg/d) intake. Milk yield, energy-corrected milk (average 35.4 kg/d), and 4% fat-corrected milk (average 33.8 kg/d) were not influenced by dietary treatments. Cows fed the control diet produced milk with less milk urea nitrogen concentration than those fed molasses-supplemented diets. Ruminal pH, NH3-N concentration, and total volatile fatty acids were not different among dietary treatments. The molar proportion of acetate linearly increased, whereas the molar proportion of propionate linearly decreased as the level of dried molasses increased. It was concluded that addition of dried molasses to high-alfalfa silage diets at 6% of the diet (dry matter basis) increased milk urea nitrogen but had no effect on animal performance.
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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) | 0.001 | 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".