Evolution of closure planning for an inactive tailings facility
Bibliographic record
Abstract
Sustainable mine closure requires meeting physical, chemical, ecological, and social objectives. Sometimes, these objectives conflict with one another and pose challenges to mine-closure planning. This paper summarizes the key considerations for closure of a tailings facility with emphasis on recent Canadian Dam Association (CDA) and International Council on Mining and Metals (ICMM) guidance. It addresses movement and drying of saturated tailings; closure; final site grading; and water management. The paper also discusses how the approach to closure, landform design, and reclamation of an inactive tailings facility has evolved since initial closure planning began, incorporating institutional knowledge and best practices in dam safety and integrated mine closure. A robust closure plan requires winnowing the options to the most attractive solution and applying a multi-staged approach to closure—one that recognizes environmental stewardship is more than just minimizing potential impacts. A brief case study of an in-progress decommissioning and closure project discusses how these principles are being applied. The case study also introduces the potential for economic benefit and resource gain for the surrounding communities through agricultural or natural-end land uses that will be considered as the design advances. The example demonstrates the benefit of reaching tailings dam sustainability goals that prioritize safety and environmental stewardship.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".