Bio-Assisted Improvement of Shear Strength and Compressibility of Gold Tailings
Bibliographic record
Abstract
Safe storage of mine tailings has challenged engineers as shown by numerous historical tailings dam failures. While the storing of tailings behind tailings dams is the most practical containment solution, poor mechanical characteristics (low strength and high compressibility) of these waste materials raises serious concerns regarding the stability of tailings dams. In this study, the microbially-induced calcite precipitation (MICP) technique is used to treat gold tailings. Accordingly, tailings were enriched with Sporosarcina pasteurii and flushed with different concentrations of cementation solution to find the CaCl2 amount that produces the highest shear strength in studied tailings. Their shear strength and compressibility were measured in direct shear and one-dimensional oedometer tests and compared with those of the untreated tailings. Results showed that the 50 mM CaCl2 cementation solution proved the most effective treatment solution with respect to MICP, reducing compressibility of tailings by about 300% when loaded up to 800 kPa and improving shear strength by 140%. X-Ray Diffraction (XRD) analysis and Scanning Electron Microscope (SEM) images of treated samples further illustrated the effects of MICP on the composition and structure of the tailings specimens.
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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 | 0.000 |
| 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) | 0.001 | 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".