Research hotspots and trends in the comprehensive utilization of distillers’ grains: A bibliometric analysis
Bibliographic record
Abstract
Distillers' grains (DGs) are industrial byproducts generated during the production of grain spirits, and their large-scale accumulation can easily lead to severe environmental pollution problems. This study provides the first comprehensive bibliometric analysis of DG-related publications from January 1940 to July 2025. The evolution of global research on DGs can be divided into four distinct stages: the initial stage (1940-1990), the emergence stage (1991-2003), the development stage (2004-2009), and the in-depth development stage (2010-2025). The results reveal that active research countries, institutions, and authors are concentrated mainly in Europe, North America, and Asia, with the United States, China, and Canada emerging as major contributors to DGs research. In particular, the United States and Canada have taken the lead in studies on the utilization of DGs. In terms of research topics, environmental issues caused by the accumulation of DGs have received increasing attention in recent years, especially in China. Current hotspots focus mainly on DGs as animal feed, including their effects on livestock health and product quality. By analysing research hotspots, trends and directions, it is expected that research on high-value utilization or deep processing of DGs by chemical methods will gradually become a popular field in the future and will become the main direction of DGs research. At present, the comprehensive utilization of DGs faces challenges such as unstable raw materials, high technical difficulty in comprehensive utilization, low economic benefits, and an incomplete evaluation standard; future efforts should therefore prioritize targeted technological innovation and fundamental research to overcome these bottlenecks. This study can provide researchers with insights for selecting topics and finding suitable research directions and can also serve as a reference for government departments in formulating plans and making funding decisions related to DGs waste management.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.111 | 0.173 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".