Mining Waste and Its Sustainable Management: Advances in Worldwide Research
Bibliographic record
Abstract
Growing social awareness of the need to adequately treat mining waste in order to protect the environment has led to an increase in the research in this field. The aim of this study was to analyze the dynamics of the research focused on mining waste and its sustainable management on a worldwide scale from 1988 to 2017. A systematic review and a bibliometric analysis of 3577 articles were completed. The results show that research into mining waste has increased, with studies focusing on waste management accounting for almost 40% of the total. The most productive journals in this field were Applied Geochemistry and Science of the Total Environment. The five most productive countries were the United States, Canada, Spain, Australia, and China. Works on the sustainable management of mining waste were in the minority, but it is an area of research that has considerable potential given the growing social awareness of the environmental repercussions of mining activities and the demands for increasingly sustainable practices. The findings of this study could prove useful for studies into mine waste, as they depict a global view of this line of research.
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.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.057 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".