A risk assessment tool for tailings storage facilities
Bibliographic record
Abstract
The recent occurrence of several major failures of tailings storage facilities (TSF) has caused the mining industry to focus on significantly improving the engineering and management (design, construction, operation and monitoring) of these structures to reduce their environmental impact. This effort is led by the Mining Association of Canada, which mandates the application of risk assessment in tailings management. Due to the very complex nature of TSF, such as phased design and construction, continuous operation, and evolving guidelines and practices over many years, the application of traditional risk assessment tools has limitations. A risk assessment tool specifically developed for TSF management is presented. This tool is based on the work of Silva et al. from 2008 that relates the annual probability of failure to the factor of safety and the level of engineering. This relationship was modified to reflect current practice. The annual probability of failure was then combined with a consequence rating to produce a rational and quantifiable evaluation of risk. The risk assessment tool provides detailed information on the level of practice of a structure, the corresponding annual probability of failure as well as the associated risk. Validation of the tool included application to a recent well-documented failure.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".