The large-scale sustainable utilization status of bauxite residue (red mud): challenges and perspectives for China
Bibliographic record
Abstract
Red mud (abbreviated as RM) is a solid waste formed during the alumina refining process from bauxite. Every year, over 200 million tons of RM are discharged worldwide. China is a large producer of alumina; the entire amount of RM of China in storage exceeds 1 billion tons because there is no technology for large-scale treatment. Extensive studies on the sustainable utilization of RM have been conducted globally in recent decades. Thus, a detailed review is provided here. According to relevant data from institutions such as the International Aluminum Association, the critical situation of production and utilization of RM from 2011 to 2022 for the world and China are analyzed. This paper uses a comprehensive literature database to classify and statistically analyze RM related publications from 2011 to 2022. The results show that research on the comprehensive utilization of RM is mainly focused on three fields of metallurgy, construction, and environment. In these fields, the main issues of not achieving large-scale production of RM in China are discussed. The results indicate that unclear responsibilities, high technical costs, lack of policies and standards, and insufficient cross-disciplinary collaboration are the main reasons. Suggestions of the utilization and development of RM have been proposed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".