Bibliographic record
Abstract
In the past 30 years, failures of mine tailing impoundments have occurred at relatively high rates, resulting in massive damage and severe economic impact to the worldwide mining industry. The rate of failure is about ten times that for conventional water retention dams. Tailings impoundments are some of the largest man-made structures, but the dams that impound tailings have only gained recognition as dams in the last few decades. This paper presented the basic features of a few case histories that provide valuable lessons to the industry. A database for tailing impoundment failures has been developed to help identify failure modes, failure impacts and failure frequency. Some clear trends emerged from this review and a better understanding of these trends can help enhance current and future design, construction and operational/closure stewardship of mine tailings facilities. This paper also summarized some of the recent initiatives by the mining industry and its regulators in helping to assure the safety of mine tailing. Many of these initiatives originated in Canada. In the past 10 years there has been a sharp increase in the amount of regulatory agencies that are setting prescriptive or rigid guidelines for tailings dams. The first step in evaluating the reasons for continued failures of mine tailings dams is to recognize the uniqueness of mine tailings facilities. The unique features include: (1) tailings impoundments are among the largest man-made structures in the world with several approaching 1 x 10{sup 9} t of stored slurried tailings, (2) tailings dams are built on a continuous basis by mine operators, and (3) tailings facilities are only a cost to the mining process. Unlike a hydroelectric dam, they do not generate a revenue stream. It was suggested that a combination of factors is responsible for the failure trends. Mining companies typically do not have in-house geotechnical expertise. Failures can have any or all of the following impacts: extended production interruption, environmental damage, damage to the industry's image, economic consequences to the industry, legal responsibility and loss of life. The author suggested that in order to make the lessons available from the tailings impoundment failure database as salient as possible, there should be some minimum expectations for the owners, designers, regulators and individuals involved in the tailings dam life cycle. 21 refs., 3 tabs., 1 fig.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".