Experiences and Future Challenges of Bioleaching Research in South Korea
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
This article addresses the state of the art of bioleaching research published in South Korean Journals. Our research team reviewed the available articles registered in the Korean Citation Index (KCI, Korean Journal Database) addressing the relevant aspects of bioleaching. We systematically categorized the target metal sources as follows: mine tailings, electronic waste, mineral ores and metal concentrates, spent catalysts, contaminated soil, and other materials. Molecular studies were also addressed in this review. The classification provided in the present manuscript details information about microbial species, parameters of operation (e.g., temperature, particle size, pH, and process length), and target metals to compare recoveries among the bioleaching processes. The findings show an increasing interest in the technology from research institutes and mineral processing-related companies over the last decade. The current research trends demonstrate that investigations are mainly focused on determining the optimum parameters of operations for different techniques and minor applications at the industrial scale, which opens the opportunity for greater technological developments. An overview of bioleaching of each metal substrate and opportunities for future research development are also included.
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