Towards sustainable mine closure and reclamation through strategic tailing disposal sites evaluation: A causality-based Dempster-Shafer framework
Bibliographic record
Abstract
Tailings, the byproducts of mineral processing, present substantial environmental risks, both physically and chemically, including potential irreversible ecological damage if not properly managed. This study introduces a hybrid decision-support framework for evaluating strategic tailings disposal sites by integrating sustainability, mine closure planning, and risk-informed governance. A key innovation is the direct linkage between tailings siting and mine reclamation strategies, offering new insights for sustainable mining. The framework combines fuzzy cognitive mapping (FCM) for causal analysis with Dempster-Shafer Theory (DST)-based evidential reasoning to address uncertainty and expert judgment variability. Twelve site selection criteria were identified from literature and expert consultation, with interdependencies modeled using a hybrid nonlinear Hebbian learning–differential evolution (NHL-DE) FCM algorithm. The approach was applied to the Zarshouran Gold Mine in Iran to support decisions in line with global tailings governance frameworks such as the Global Industry Standard on Tailings Management (GISTM). DST was used to compute belief degrees for alternative sites, reflecting environmental, technical, and socio-economic factors. Results revealed that “Socioeconomic impact on downstream communities” and “Engineering challenges” were the most influential criteria (both with normalized causal weights of 0.110). The most favorable site, located near the processing plant, achieved an 84.6 % belief degree at the “Good-Excellent” level, followed by a site near the pit (82.4 %). By streamlining methodology and emphasizing site-specific insights, the framework offers a robust tool for prioritizing tailings sites under uncertainty, aligning technical evaluations with ESG principles and mine closure objectives.
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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".