Tailings-flow runout analysis: Examining the applicability of a semi-physical area–volume relationship using a novel database
Bibliographic record
Abstract
Abstract. Tailings-flows result from the breach of tailings dams. Large-scale tailings-flows can travel over substantial distances with high velocities and cause significant life loss, environmental damage and economic costs. Runout modelling and inundation mapping are critical components of risk assessment for tailings dams. In an attempt to develop consistency in reporting tailings data, we established a new tailings-flow runout classification system. Our data analysis applies to the zone corresponding to the extent of the main solid tailings deposit, which is characterized by visible or field-confirmed sedimentation, above typical surface water levels if extending into downstream water bodies. We introduced a new database of 33 tailings dam breaches by independently estimating the planimetric inundation area for each event using remote sensing data. This paper examines the applicability of a semi-physical area–volume relationship using the new database. Our results indicate that the equation A = cV2/3, which has been used previously to characterize the mobility of other types of mass movements, is a statistically-justifiable choice for the relationship between total released volume and planimetric inundation area. Our analysis suggests that, for a given volume, tailings-flows are, on average, less mobile than lahars but more mobile than non-volcanic debris flows, rock avalanches and waste dump failures.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".