What are the real risks for tailings facilities?
Bibliographic record
Abstract
The movement towards risk-based design and operation of tailings storage facilities (TSFs) has taken place over the last few decades. The establishment of the consequence of failure of a facility is used to determine the design criteria to be used in its design. These criteria generally set the acceptable return periods for seismic and hydrologic events that the facility must accommodate. In addition, there are generally several levels of risk assessment of the design carried out to highlight technical risks that require particular attention and controls to manage. These are usually addressed in the design phase of project development. Despite this focus on technical risk assessment at the design phases of the development of a TSF, there is still a significant number of failures occurring every year. In recent times, there have been a number of high profile TSF failures in facilities owned by major mining houses and/or located in highly regulated, first world countries. In almost every case, the investigations into the failures have been carried out by high profile, internationally recognised geotechnical engineers who have identified the technical reasons for the failure. In many cases, it has been shown that the root causes of the failures have been a failure in governance, capital constraints, change management, independent reviews, construction supervision, operation, etc. The investigation of failures and reports to the public are almost exclusively focused on the technical cause with much less focus on what is often the underlying root cause. A number of international mining industry groups have recognised the lack of effective governance as being a major risk that could lead to TSF failures. The Mining Association of Canada (MAC) and the International Council on Mining and Metals (ICMM) are two examples. In this paper, the various methods for risk assessment and management are described. Non-technical risks that arise in the design and operation of TSFs are discussed and importance of good governance and continuity of its application during the full lifecycle of the facilities is emphasised.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".