Promoting sustainable mining for health, food security and biodiversity conservation
Bibliographic record
Abstract
Abstract With approximately 25,000 active mining companies operating in 140 countries, the mining industry significantly contributes to global economic growth but also poses severe environmental and social challenges. This review examines the implications of mining on human health, food security, and biodiversity conservation, drawing insights from case studies and the Towards Sustainable Mining (TSM) initiative. The extraction and processing of minerals such as coal, gold, silver, copper, and zinc release hazardous materials, including arsenic, mercury, lead, and radioactive substances, leading to soil contamination, water pollution, and biodiversity loss. Mining-related emissions have been linked to respiratory diseases, neurological disorders, and increased mortality rates in affected communities. In agriculture, land degradation and competition for resources reduce crop productivity, with nearly 90% of food insecurity hotspots in Africa coinciding with mining sites. Biodiversity loss results from habitat destruction, soil contamination, and the introduction of invasive species, disrupting ecological balance. The study highlights regulatory frameworks and sustainable mining approaches, such as mercury-free gold mining in Ghana and corporate responsibility programs in Canada, Finland, and Spain. Future directions emphasize the need for innovative technologies, stricter environmental policies, and increased stakeholder engagement to mitigate mining’s adverse effects while ensuring resource sustainability.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".