Effects of Barley Silage Chop Length on Productivity and Rumen Conditions of Lactating Dairy Cows Fed a Total Mixed Ration
Bibliographic record
Abstract
Barley silage, cut at the early dough stage, was chopped long (19 mm) or short (10 mm), ensiled, and incorporated into total mixed rations (TMR). The TMR contained (dry matter [DM] basis) either 58.0 or 41.4% concentrate and either short- or long-chopped barley silage. Reducing chop length of barley silage decreased the proportion (asis basis) of TMR particles retained by the 8- and 19-mm screens of the Penn State Particle Separator (PSPS) from 66.9 to 52.7% in the high concentrate TMR and from 74.8 to 60.9% in the low concentrate TMR. Chop length reduction decreased dietary physically effective fiber, calculated as the NDF retained by the 8- and 19-mm screens of the PSPS, from 29.2 to 25.2% DM in the high concentrate TMR and from 34.9 to 30.6% DM in the low concentrate TMR. Reduction in chop length did not affect rumen pH, total rumen volatile fatty acids, milk yield, and milk composition, but increased DM intake from 19.4 to 20.1 kg/d at the high level of concentrate and from 16.9 to 17.7 kg/d at the low level of concentrate and increased rumen propionate. Increasing the concentrate inclusion rate reduced rumen pH from 6.52 to 6.35, did not affect total volatile fatty acids, reduced the acetate-to-propionate ratio from 3.1 to 2.7, increased milk yield from 28.7 to 31.3 kg/d, reduced milk fat content from 3.48 to 2.94%, and increased milk protein content from 3.11 to 3.27% across chop lengths.
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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 | 0.000 | 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".