Evaluation of the liquefaction potential of filtered tailings stacks by means of laboratory investigation
Bibliographic record
Abstract
Filtered tailings are typically transported, placed, and compacted into embankments to form unsaturated, dense stacks. For economic reasons, these stacks must be raised with maximum productivity and minimal possible cost. Achieving this may involve using thicker compaction layers than in conventional embankments, along with employing bulldozers and trucks in place of rollers. However, increasing the layer thickness often leads to lower compaction within individual layers. To assess the geotechnical behavior of filtered tailings, laboratory testing was conducted on three layers of varying thickness within a test embankment. The results were evaluated within a critical state soil mechanics framework to assess the susceptibility of filtered tailings to static liquefaction. The degree of compaction (DC) was found to play a crucial role in determining the mechanical behavior of the filtered tailings. Samples with lower compaction levels showed a reduction in post-peak shear strength during undrained tests, making them more susceptible to liquefaction. The instability line was found to be sensitive to DC in all layers. The undrained brittleness index was also calculated to assess the potential loss of shear strength due to liquefaction. Therefore, ensuring adequate compaction is crucial to preventing the liquefaction of filtered tailings and maintaining the stability of the embankments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".