Molecular Structure and Metabolic Characteristics of the Proteins and Energy in Triticale Grains and Dried Distillers Grains with Solubles for Dairy Cattle
Bibliographic record
Abstract
To our knowledge, there is no research on the molecular structure of triticale grain in comparison with other types of cereal grains and metabolic characteristics of the protein and energy in this grain and its coproducts, called dried distillers grains with solubles (DDGS), for dairy cattle. The objective of this study was to identify differences in molecular structures of proteins among grains and their DDGS using a molecular spectroscopy technique, namely, DRIFT, and to determine the nutrient profile and supply to dairy cattle. The protein molecular structure studies showed a difference (P < 0.01) in the amide I to amide II ratio and the α-helix to β-sheet ratio between grains and their DDGS. The energy content was similar for triticale grain and DDGS. There were differences in the protein and carbohydrate subfractions (P < 0.05) and the ruminal degradability of DM, CP, and NDF (P < 0.01) between triticale grain and DDGS. Triticale grain and DDGS had similar intestinal digestibility of rumen undegraded CP. However, triticale DDGS had higher (P < 0.01) predicted total metabolizable protein and degraded protein balance than triticale, indicating that triticale DDGS is a superior protein source for dairy cattle as compared with triticale grain. Bioethanol processing induced changes in the protein molecular structure.
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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) | 0.000 | 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".