Effects of processing methods (rolling vs. pelleting vs. steam-flaking) of cool-season adapted oats on dairy cattle production performance and metabolic characteristics compared with barley
Bibliographic record
Abstract
Several processing techniques can be used to slow the degradation rate in the rumen and thus provide more bypass crude protein (CP) and starch to the small intestine. The aim of this study was to evaluate the effects of processing methods on cool-season adapted oat grain compared with dry-rolled barley grain, when fed as total mixed ration (TMR) for lactating dairy cows. Eight lactating Holstein cows were used in a replicated 4 × 4 Latin square design with 21-d periods and fed TMR with 1 of 4 treatments: dry-rolled oats, steam-flaked oats, pelleted oats, or dry-rolled barley. Dry matter intake (DMI) ranged from 28.19 to 31.61 kg/d and was lower for rolled oats compared with pelleted oats. Despite the nutrient intake being higher for cows fed pelleted oats, those fed rolled oats had the highest milk production and milk fat percentage (49.23 kg/d and 4%, respectively). Ruminal fermentation characteristics were similar across treatments, with only significant differences in concentrations of acetate (lowest for pelleted oats) and total short-chain fatty acids (highest value for rolled barley) and a lower pH for flaked oats at the 9-h and 12-h points. Dietary treatments did not affect total-tract digestibility of dry matter, organic matter, or CP; digestibility of starch was the lowest for rolled barley (89.04%). Measured blood metabolites, urea, glucose, and β-hydroxybutyrate, were not affected by dietary treatment. Purine derivatives and microbial N supply were also unaffected by dietary treatments. Cows fed flaked oat-based TMR showed the lowest N excretion in milk; however, the lack of difference between diets with regard to urinary N and fecal N excretion resulted in no significant changes in N balance between diets. Therefore, rolled oats allow cows to have higher milk production with lower DMI compared with all other treatments in this study.
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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) | 0.001 | 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".