Prediction of protein supply to ruminants from concentrates: comparison of the NRC‐2001 model with the DVE/OEB system
Bibliographic record
Abstract
Abstract The objective of this study was to compare the DVE/OEB system (DVE = truly absorbed protein in the small intestine; OEB = degraded protein balance) and the NRC‐2001 model in the prediction of supply of protein to dairy cows from 46 selected concentrates: malting‐type barley (cv Harrington), feed‐type barley (cv Valier), field tick beans (Vicia faba), white albus lupins (Lupinus albus), whole soybeans and horse beans (Vicia faba cv Alfred). The two barleys were processed by coarse (roller miller, 0.533 mm gap) and fine (hammer mill, 2 mm screen) processing. The field tick beans and white albus lupins were dry roasted at various conditions at the University of Melbourne. The soybeans and horse beans were pressure‐toasted at 100, 118 and 136 °C for 3, 7, 15 and 30 min at Wageningen Feed Processing Centre. Comparisons were made in terms of (1) ruminally synthesized microbial protein, (2) truly absorbed protein in the small intestine and (3) degraded protein balance, based on 46 samples. The results showed that the predicted values from the DVE/OEB system and the NRC‐2001 model had significant correlations. However, using the DVE/OEB system, the overall average microbial protein supply based on available energy was 10% lower and the truly absorbed protein in the small intestine was 8% lower than that predicted by the NRC‐2001 model. A difference was also found in the prediction of the degraded protein balances, which was 16% higher than that estimated from the NRC‐2001 model. These differences are due to factors used in calculations in the two models, although both are based on similar principles. This indicates that further refinement is needed for a modern protein evaluation and prediction system. Copyright © 2004 Society of Chemical Industry
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".