Modelling the metabolic characteristics of proteins in dairy cattle from co‐products of bioethanol processing: comparison of the NRC 2001 model with the DVE/OEB system
Bibliographic record
Abstract
BACKGROUND: Co-products from bioethanol processing include wheat dried distillers grains with solubles (DDGS), corn DDGS, blend DDGS (e.g. wheat/corn at 70:30, 60:40 or 50:50 w/w), triticale DDGS, barley DDGS and pea DDGS. The objective of this study was to compare two systems, the DVE/OEB system versus the NRC 2001 model, in modelling the metabolic characteristics of proteins in dairy cattle from different types of co-products (DDGS) from different bioethanol processing plants. RESULTS: The predicted values from the NRC 2001 model were 10% higher (P < 0.05) in truly absorbable rumen-synthesised microbial protein in the small intestine, 10% lower (P < 0.05) in truly absorbed rumen-undegraded feed protein in the small intestine, 30% lower (P < 0.05) in endogenous protein and 2% lower (P < 0.05) in total truly absorbed protein in the small intestine than the predicted values from the DVE/OEB system. However, no significant difference was detected in terms of the degraded protein balance between the two models (P > 0.05). CONCLUSION: The sensitivity of the two models in detecting differences among DDGS types and between bioethanol plants was similar. The two models coincided in the superior protein value of blend DDGS as well as in the more optimal degraded protein balance (DPB) for corn DDGS. Although the differences between the DVE/OEB system and the NRC 2001 model were significant (P < 0.05) for most outputs owing to differences in some of the concepts and factors used in modelling, the correlations between total truly absorbed protein (DVE) and metabolisable protein (MP) values and between degraded protein balances (DPB(OEB) vs DPB(NRC) ) were also significant (P < 0.05).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".