The Water Management Impacts of Large-Scale Mining Operations: A Social and Environmental Perspective
Bibliographic record
Abstract
This study investigates water consumption in two areas with limited water resources—the Salar de Atacama and Salar de Atacama-Vertiente Pacifico basins in Chile’s Antofagasta Region—with the aim of developing strategies that incorporate social and environmental aspects into water management. A qualitative approach was employed that involved a focus group with twelve water management representatives and surveys of the general population (468 responses). Additionally, the current state of water rights in the two basins was examined and the feasibility of the proposed strategies was assessed. The findings reveal that the mining industry’s development approach is mostly viewed as negative, mainly due to inadequate community engagement, confidential consumption data, and limited government oversight. The quantitative findings indicate that 53.8% of respondents see the main obstacle as the lack of a solution satisfying both parties. Additionally, 35.3%, 24.4%, and 22.4% believe transparency, objective information provision, and detailed resource usage disclosure by mining companies would help. Adopting a comprehensive water stewardship approach that considers social and environmental factors would enable a novel contribution to a more effective and sustainable water resource management system in northern Chile, mitigating communities’ negative perceptions of the industry and facilitating the integration of communities and involved agents. Therefore, improved management and transparent collaboration among stakeholders are essential for responsible water resource use in mining.
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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 | 0.001 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".