Variability in Distillers’ Co-Product Compositions and Their Nutritional Availability for Pigs: Insights from a Systematic Literature Review
Bibliographic record
Abstract
This study aimed to examine the nutritional diversity of distillers' co-products in pigs. Two distinct databases were used in this study. The first was compiled through a systematic review of previous scientific publications that have evaluated the chemical composition and nutritional availability of these ingredients for pigs. The final database included information collected from 49 studies published between 2003 and 2022. Most of the studies focused on distillers' co-products produced from corn (90%) and assessed dried distillers grains with solubles (DDGS; 92%). A wide variety of nutritional compositions was observed in the selected studies, with coefficients of variation of 8% for gross energy and 13% for crude protein in dried distillers grains (DDGs). In the general database, half of the studies were published from 2003 to 2015 and the other half from 2015 to 2022. Greater variability in the net energy content and standardized ileal digestible lysine concentrations from 2015 to 2022 were the main concerns. The second database included information on high-protein dried distillers grains (HP-DDGs) collected directly from several feed mills and was built using the results of chemical analyses. These results suggest that the nutritional matrix of co-products should be assessed for each processing plant. Nevertheless, a coefficient of variation of up to 6% was found for crude protein and up to 20% for ether extract. Variability in nutritional composition is an important challenge of using distillers' co-products in pig feed.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".