Physiochemical characterization and energy contents of novel corn ethanol co-product streams, with and without inclusion of a multi-carbohydrase enzyme blend, for growing pigs
Bibliographic record
Abstract
The physiochemical properties and digestible, metabolizable, and predicted net energy contents in high-protein dried distillers’ grain (HiPro) were determined to assess the nutritive value for growing pigs. Twelve Yorkshire × Landrace barrows (initial body weight 25 ± 0.5 kg) were used in a partially replicated Latin square design over three periods (n = 7 or 8) and assigned to one of five experimental diets. In each period, pigs were adapted to diets for 7 d, followed by 5 d of total urine collection and fecal grab sampling. The experimental diets included a corn- and soybean-meal-based diet (CON) or diets containing dried distillers’ grains with solubles (DDGS) or HiPro to partially replace corn and soybean meal, without or with (i.e., DDGS+ and Hipro+) a multi-carbohydrase enzyme blend (0.05% inclusion). The HiPro ingredient contained half as much starch (2.6% vs. 5.2%; DM-basis), 20% more protein (32.5% vs. 27.1%), and had 14% greater water binding capacity versus DDGS. The digestible, metabolizable, and predicted net energy contents of the HiPro co-product were greater than DDGS for growing pigs (P < 0.05), but fibre-degrading enzymes were ineffective at improving energy values. The greater (available) energy and protein contents of HiPro make it a promising feed ingredient for inclusion in swine diets.
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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".