Use of Distillers Grains Co-Products in Feedlot Diets in the U.S. and Canada
Bibliographic record
Abstract
Continued expansion of the ethanol industry in the United States and Canada will have a direct impact in two distinct areas of beef production. First, ethanol production is an important end user of traditional feedstuffs used in beef production (corn, sorghum, wheat), and second, increased production of ethanol will result in an increase in the supply of ethanol co-products. The majority of ethanol plant expansions appear to be dry milling plants, due primarily to their relative simplicity when compared with the wet milling process. The dry milling co-product, referred to as distillers grains (DG), can be fed wet (WDGS; 35 to 50% DM) or dry (DDGS; >88% DM) with or without solubles. Based on the current process of ethanol production from corn grain, all non-starch nutrients are concentrated 300% in distillers grains compared with the original corn grain. Important nutrients to consider in feedlot diet formulation include protein, ether extract (EE), phosphorus (P) and sulfur (S). These nutrient considerations can be grouped into three main categories of interest, which include environmental (protein and P), sulfur toxicity (S) and supplemental fat (EE). Environmental concerns can be mitigated with a sound nutrient management plan. Sulfur levels in DGS should be monitored as they are likely variable and can be quite high. Corn, sorghum and wheat DGS should contain approximately 12.18, 9.09 and 7.02% fat, respectively, suggesting corn DGS will be of greater value in feedlot diets when DG are fed as an energy source compared with either sorghum or wheat DGS. Other than the type of DGS that is being fed, current information regarding feedlot performance suggests the optimum level of DGS is finishing diets is affected by inclusion level of the DG product, grain processing, roughage level, and perhaps inclusion of ionophores and antibiotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".