A unified metric for costing tailings dams and the consequences for tailings management
Bibliographic record
Abstract
Early-stage decision making on tailings disposal technology has significant and long-lasting impacts on mine economics and risk. The assumed favorable economics of slurried tailings disposal has had wide-reaching implications on the uptake of dewatered tailings technology such as paste thickeners, dry-stack and cyclone tails. This paper addresses the need for a comparable metric across tailings disposal options by the development of a financial model and unified costing metric which can be used during a mine's initial design choice and decision-making stages. The financial model developed estimates the actual cost of tailings dams in USD per dry metric tonne working within the framework of Canadian disclosure requirements. The method's utility is illustrated by applying the model to a case study of a Chilean copper mine. This case study demonstrates the usefulness of a unified metric for application in mine development proposals to improve the financial reporting transparency of TSFs. A unified cost metric would result in companies assessing their financial obligations more systematically, thereby promoting decision-making around alternatives to tailings dams in the longer term.
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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 | 0.006 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".