Responsible Recovery of Mine Tailings: A Governance-Driven Framework for Intelligent Green Mining
Bibliographic record
Abstract
The transition to intelligent, low-carbon mining requires turning solid-waste liabilities into strategic resources. This study develops a Responsible Recovery framework for the disposal and utilisation of mine tailings, integrating global governance standards, advanced process technologies, and emerging AI tools. Using critical-material tailings as a test domain, the framework connects risk classification, disclosure, and flowsheet design to auditable performance metrics. Intelligent modules - from site-inventory parsing and flowsheet recommendation to automated validation of ESG datasets - show how artificial intelligence can improve technical accuracy while strengthening transparency. The approach supports circular recovery of cobalt, nickel, and rare-earth-bearing residues, reducing both waste and import dependence. By aligning digital innovation with established standards such as the Global Industry Standard on Tailings Management (GISTM) and that of the Mining Association of Canada (MAC), the paper shows how tailings management can evolve into a governed, data-driven pathway for recovering critical materials within the wider Intelligent Green Mining agenda.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".