Advanced tailings dam performance monitoring with seismic noise and stress models
Bibliographic record
Abstract
Abstract Tailings dams retain the waste by-products of mining operations and are amongst the world’s largest engineered structures. Recent tailings dam failures highlight important gaps in current monitoring methods and a pressing need to advance research on tailings dam monitoring technologies, considering growth predictions for the mining of metals. At an active tailings dam in northern Canada, we combine ambient noise interferometry with a quantitative stress model to monitor shear wave velocity (Vs) changes. Changes in seismic velocities of less than 1% correlate strongly with water level fluctuations at the adjacent tailings pond. A stress model, calibrated using pond level recordings and Vs profiles obtained from cone penetration tests, demonstrates that the seismic velocity changes obtained with ambient noise interferometry are predominantly changes in Vs. Furthermore, this model constrains Vs changes to a depth of ~16 m, corresponding to uncompacted tailings below the dam. As Vs is used to assess the liquefaction potential of soils, this method provides important advances for understanding changes in dam performance over time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".