The environmental and economic case for valuing water recovery and its relationship with tailings storage conservation
Bibliographic record
Abstract
Alternative tailings disposal methods such as dry stack are often neglected as optimal mine waste disposal methods due to perceived high cost. However, the transition from traditional wet tailings disposal to dry stack and alternative tailings disposal is critical for decreasing freshwater consumption and de-risking projects. This research presents an open-source techno-economic model which allows for the comparison of water use and cost between different tailings disposal-related equipment, addressing a major gap in models available for operators and early technology decision-makers. A case study of a Chilean copper mine is evaluated through the model to optimize the dewatering equipment choice. The findings indicate a substantial technical, environmental and economic return on the thickener control system and hydro-cyclone compared to desalinated and pumped water. Early economic modeling can quickly develop a business case for new water and energy-saving technologies while creating shared de-risked technical, ESG, and business team drivers.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".