Influence of Particle Size on the Effectiveness of the Fiber in Barley Silage
Bibliographic record
Abstract
We used eight multiparous Holstein cows in a 4 x 4 Latin square design to evaluate the effects and possible interactions between silage particle size and concentrate level on chewing activities and productivity of cows fed barley-based total mixed rations (TMR). Diets were designed with two forage-to-concentrate ratios (low forage, 45:55, high forage 55:45), combined with two theoretical chop lengths of barley silage (short = 4.68 mm and long = 18.75 mm). Diets were formulated to provide similar and above-minimum neutral detergent fiber recommended for cows in early lactation. Increasing silage particle size of the forage did not affect dry matter intake. The 3.5% fat-correct milk and fat yields trended higher for increased particle size. Percent milk protein was higher for short particle size. Increasing the concentrate levels in the diets increased proportions of milk protein and lactose, but not milk fat. Cows fed short silage spent 90 min less per day chewing and ruminating than did those on long silage. Total chewing activity per kilogram of forage intake was higher for cows on long silage compared with those on short silage diets. Although a reduction in silage particle size did not depress milk fat, rumination and chewing activity were significantly reduced. These results suggest that particle size of the silage may have dominant control over chewing activity despite adequate neutral detergent fiber intakes.
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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".