Assessment on Sustainable Biomining: Integrating Environmental Responsibility and Economic Viability
Bibliographic record
Abstract
ABSTRACT Mining has long been a crucial for industrial and economic development, yet conventional practices have led to environmental degradation, resource depletion, and social challenges. Biomining has emerged as a sustainable alternative, utilizing microorganisms for metal extraction and environmental restoration. This eco‐friendly approach facilitates the recovery of metals from low‐grade ores and mining waste while reducing energy consumption, greenhouse gas emissions, and environmental impact. This review provides a comprehensive analysis of biomining's economic, environmental, and social implications, emphasizing its role in advancing the circular economy. Global case studies from Chile, China, Canada, and South Africa illustrate its feasibility and benefits. Various biomining techniques, including heap leaching, stirred‐tank bioleaching, and in situ biomining, are examined for their effectiveness in recovering metals like copper, gold, and uranium. Furthermore, innovations in microbial genomics and bioelectrochemical systems highlight the potential of engineered microorganisms to enhance metal recovery. Despite its promise, biomining faces challenges such as slow processing rates, microbial adaptation issues, and regulatory barriers. Future advancements, including synthetic biology, artificial intelligence, and policy‐driven incentives, could optimize biomining applications worldwide. This review underscores biomining's potential to bridge scientific innovation and industrial sustainability, ensuring responsible resource management and reduced environmental impact.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".