Modeling Nutrient Supply to Ruminants: Frost-damaged Wheat vs. Normal Wheat
Bibliographic record
Abstract
The objectives of this study were to use the NRC-2001 model and DVE/OEB system to model potential nutrient supply to ruminants and to compare frost damaged (also called "frozen") wheat with normal wheat.Quantitative predictions were made in terms of: i) Truly absorbed rumen synthesized microbial protein in the small intestine; ii) Truly absorbed rumen undegraded feed protein in the small intestine; iii) Endogenous protein in the digestive tract; iv).Total truly absorbed protein in the small intestine; and v).Protein degraded balance.The overall yield losses of the frozen wheat were 24%.Results showed that using the DVE/OEB system to predict the potential nutrient supply, the frozen wheat had similar truly absorbed rumen synthesized microbial protein (65 vs. 66 g/kg DM; p>0.05), tended to have lower truly absorbed rumen undegraded feed protein (39 vs. 53 g/kg DM; p<0.10) and had higher endogenous protein (14 vs. 9 g/kg DM; p<0.05).Total truly absorbed protein in the small intestine was significantly lower (89 vs. 110 g/kg DM, p<0.05) in the frozen wheat.The protein degraded balance was similar and both were negative (-2 vs. -1 g/kg DM).Using the NRC-2001 model to predict the potential nutrient supply, the frozen wheat also had similar truly absorbed rumen synthesized microbial protein (average 56 g/kg DM; p>0.05), tended to have lower truly absorbed rumen undegraded feed protein (35 vs. 48, g/kg DM; p<0.10) and had similar endogenous protein (average 4 g/kg DM; p>0.05).Total truly absorbed protein in the small intestine was significantly lower (95 vs. 108 g/kg DM, p<0.05) in the frozen wheat.The protein degraded balance was not significantly different and both were negative (-16 vs. -19 g/kg DM).In conclusion, both models predict lower protein value and negative protein degraded balance in the frozen wheat.The frost damage to the wheat reduced nutrient content and availability and thus reduced nutrient supply to ruminants by around 12 to 19%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".