Short communication: Effects of feeding pearl millet silage on milk production of lactating dairy cows
Bibliographic record
Abstract
Twenty Holstein cows were used in a randomized complete block experiment to determine the feeding value of pearl millet silage (MS) for dairy cows relative to corn silage (CS). Two isonitrogenous diets were formulated with a 53:47 forage:concentrate ratio. Pearl millet silage and CS comprised 67% of the forages in each diet. Relative to CS, MS contained higher crude protein (13.0 vs. 9.4%), neutral detergent fiber (66.9 vs. 40.7%), and acid detergent fiber (38.8 vs. 23.9%). Dietary treatments had no effect on dry matter (average = 23.9 kg/d) or crude protein (average = 4.2 kg/d) intake. However, cows fed MS consumed more neutral detergent fiber (9.7 vs. 8.3 kg/d) than did cows fed CS. Silage type had no effect on milk yield (average 38.0 kg/d), whereas energy-corrected milk (43.8 vs. 38.6) and 4% fat-corrected milk (41.8 vs. 35.5 kg/d) were greater for cows fed MS than for those fed CS. Milk protein, lactose, and total solids concentrations were not influenced by dietary treatments. However, cows fed MS produced milk with a greater milk fat concentration (4.17 vs. 3.78%) than did cows fed CS. We concluded that MS, when compared with CS, had a similar effect on feed intake, milk yield, and milk efficiency. Because of increased milk fat concentration, cows fed MS produced more energy-corrected milk than did cows fed CS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".