Technical and economic assessment of dry stack tailings alternatives for an iron ore project: a case study
Bibliographic record
Abstract
With a combination of highly publicised tailings dam failures and acute water shortages in some locations around the world, mining projects are facing more stringent requirements relating to safety, sustainability and environmental acceptance. This paper focuses on a conceptual study related to a dry stack tailings (DST) project for an iron ore operation in North America. The study consisted of two parts: firstly, the effect material properties have on the process selection and deposition of the tailings, and secondly, the dewatering, transport and stacking alternatives (taking into account the topography of the permitted tailings storage located in a mountainous area). The case study also included the overall investment and operating cost estimation for the storage of approximately 70 Mt of tailings over a period of nine years. The paper describes, step by step, evaluation of the key input parameters, the development of a time usage model in connection with the selected operating schemes, and the equipment required for the processing, transport and stacking of the tailings.
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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".