Research progress on acid mine drainage treatment based on CiteSpace analysis
Bibliographic record
Abstract
Acid mine drainage has always been of global concern, primarily due to its low pH, high concentration of heavy metals and toxic substances, and serious impact on the surrounding environment and ecology of mines. However, the research progress and hotspots in this field of acid mine drainage processing are still unclear. To better understand the research hotspots and trends of acid mine drainage processing from 2004 to 2023, we used CiteSpace bibliometric software to visually analyze 1142 English-language research articles and reviews from the Web of Science core database. Results indicated that this field has received increas-ing attention from researchers worldwide, especially since 2017. The USA and China stand out as major contributors, yet their international collaboration doesn't match South Africa robust partnerships. Strengthening cooperation with other nations should be a priority for both the USA and China. The University of Quebec and University of South Africa were the most production institution. Vhahangwele Masindi from South Africa was the most active author. The top two core journals in this field were Science of the Total Environment and Water Re-search. Additionally, through keyword co-occurrence, clustering, and burst analysis, it is evi-dent that research on heavy metal mechanisms and resource recovery will be the future re-search hotspots in this field of acid mine drainage. This study provides researchers with an opportunity to understand the hotspots and trends in acid mine drainage research from a bibliometric perspective, and serves as a reference for future studies.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.082 | 0.108 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".