Effects of partially replacing barley or corn with raw and micronised CDC SO-I oats on productive performance of lactating dairy cows
Bibliographic record
Abstract
Recently, a new genotype of oat (cv. CDC SO-I, containing low-hull lignin and high-fat groat), has been developed. The objective of this study was to determine the effects of partially replacing barley and corn with the new oat and its micronisation on lactating performance of dairy cows. In a double 4 x 4 Latin square design, eight lactating dairy cows (732 +/- 46 kg body weight [BW]; parity 4 +/- 2) received total mixed rations with a forage-to-concentrate ratio of 50:50 (DM basis). The four treatments were: T1, barley only (control); T2, raw oat, replacing 42% barley of T1; T3, micronised oat, replacing 42% barley of T1; and T4, raw oat and corn blend, replacing 100% barley of T1. The results showed that dairy cows fed the new oats (T2, T3) produced more fat (p < 0.05) and more fat corrected milk (p < 0.10) than cows fed barley only (T1). The performance of cows fed the new oat and corn blend (T4) was not significantly different from other treatments. The micronisation significantly reduced protein degradability (74 vs. 63%,p < 0.05), but increased starch degradability (87 vs. 93%,p < 0.05) of the new oat. However, the overall results suggested that micronisation did not show a significant impact on milk production. The newly developed CDC SO-I oat can replace 42% barley (in T1) as a concentrate supplement in dairy total mixed rations with an increased yield of milk fat and fat corrected milk.
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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) | 0.001 | 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) | 0.000 | 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".