Identifying Dietary Fiber Components that Best Predict the Digestible and Metabolizable Energy Content in Nine Corn Co-Products Fed to Growing Pigs
Bibliographic record
Abstract
An experiment was conducted to determine the best dietary fiber (DF) assay to estimate the effect of DF concentration on energy digestibility, and to predict the digestible (DE) and metabolizable (ME) energy of 9 corn co-products: conventional corn bran, corn bran with solubles, corn distillers dried grains with solubles (DDGS) conventionally produced, reduced oil DDGS, uncooked DDGS, high protein distillers dried grains, dehulled degermed corn, corn germ meal, and corn gluten meal. The apparent total tract digestibility (ATTD) of gross energy (GE), and the DE and ME of the 9 feed ingredients were determined in 20 growing pigs (BW = 25.9 ± 2.5 kg). Feed ingredients were analyzed for the concentration of dietary fiber: NDF, TDF, total non-starch polysaccharides (NSP), and 5 constituent monosaccharides of NSP, namely arabinose (Ara), xylose (Xyl), mannose (Man), glucose (Glc), and galactose (Gal). The concentration of xylose in NSP was the DF assay that best explained variation due to DF concentration in digestibility of energy, DE, and ME values, and can be used to predict the DE and ME values in corn co-products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".