Precision processing barley grain did not affect productivity of lactating dairy cows
Bibliographic record
Abstract
Schlau, N., Duineveld, L., Yang, W. Z., McAllister, T. A. and Oba, M. 2013. Precision processing barley grain did not affect productivity of lactating dairy cows. Can. J. Anim. Sci. 93: 261–268. This study evaluated the effects of precision processing (PP; processing based on kernel size) barley grain on ruminal fermentation and productivity of lactating dairy cows. Twenty multiparous Holstein cows, including eight ruminally cannulated cows, were used in a replicated 4×4 Latin square design with 21-d periods. Diets contained light barley grain processed precisely using a narrow roller setting (LB), heavy barley processed precisely using a wide roller setting (HB), processed HB and LB mixed at equal proportions (PP), or equal parts of light and heavy barley grain processed at a single narrow roller setting (CON). All diets consisted of 40% barley grain, 40% barley silage, and 20% of a supplement premix. Comparisons were made between LB and HB to evaluate the effect of barley quality, and between PP and CON to evaluate the effect of precision processing. Dry matter intake, sorting index, ruminal fermentation characteristics, and nutrient digestibility were not affected by diet. In addition, milk yield and concentrations of milk fat, protein, and lactose were not different, although milk urea nitrogen concentration was greater for PP vs. CON and for LB vs. HB. These results suggest that precision processing barley grain based on kernel size may not drastically affect ruminal fermentation and milk production in lactating dairy cows.
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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 | 0.001 | 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".