Catastrophic mass flows resulting from tailings impoundment failures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tailings dam failures have received significant attention in recent years due to the catastrophic downstream consequences, as evidenced by the 2019 Feijão disaster in Brazil and numerous precedents. This paper presents a timely review of tailings flows with the support of a comprehensive global database of 63 cases that have been remotely analyzed through a compilation of satellite imagery, digital elevation models and literature. The synthesis provides insight into the influence of impoundment conditions, preconditioning and trigger variables, failure mechanisms and the downstream environment on tailings flow behaviour. The database also sheds light on the limitations of data quality and availability in the public domain. Magnitude-frequency statistics indicate that tailings dam breaches that have produced catastrophic mass flows with total outflow volumes of ≥1 M m3 have occurred at a mean recurrence interval of 2–3 years over the period 1965–2020. Weather hazards and impoundment drainage issues are identified as major causative variables. The occurrence of liquefaction and/or the incorporation of free water are sufficient conditions to trigger extremely rapid, highly mobile behaviour. Travel path confinement and steeper bed slopes enhance flow velocities (peak of 25–30 m/s) and kinetic energy, whereas flow mobility appears to be exacerbated along major rivers. Although general trends may be observed in empirical observations, such efforts are prone to substantial uncertainty due to the complexity and variability of site conditions (that are typically unaccounted for in broad statistical approaches) as well as poor data availability and/or quality for many of the selected cases. This highlights the importance of performing site-specific investigations through numerical models, laboratory tests and field observations to better predict post-breach behaviour (ideally within a probabilistic framework) when undertaking site assessments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it