The large-scale sustainable utilization status of bauxite residue (red mud): challenges and perspectives for China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it