Developing an operation, maintenance and surveillance manual for the post-closure management of tailings facilities
Bibliographic record
Abstract
Mining companies have become increasingly focused on developing environmentally responsible decommissioning and closure techniques of tailings facilities to work towards more sustainable mining practices. With the help of the Mining Association of Canada (MAC) guidelines, the development and implementation of functional and manageable Operation, Maintenance and Surveillance (OMS) manuals for operating tailings facilities have become commonplace. As more tailings facilities are decommissioned and closed, and the expectations of regulators and society continue to grow, the need for further guidance through the application of similar documentation for the management of the post-closure facility also increases. This paper highlights the importance of developing a post-closure OMS manual for the management of closed tailings facilities (and other mining landforms), by way of a post-closure OMS guidebook. It describes initial ideas in the development of post-closure OMS manuals and provides some reasoning, structure and contents for a post-closure OMS manual. It would be encouraged that tailings facility reclamation personnel utilise a universal post-closure OMS guidebook to prepare a site-specific post-closure OMS manual. This paper references a range of sources and first hand experiences by the authors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".