Comparison of geomechanical behavior of an iron ore tailings and natural siliceous silty sand with the same particle size distribution
Bibliographic record
Abstract
The safe design of tailings storage facilities requires an in-depth understanding of mine tailings’ geomechanical behavior. Several research studies have focused on mine tailings characterization, sometimes using large databases to derive performance trendlines. However, it is often difficult to distinguish which observed patterns are specific to mine tailings being different from natural sands. This paper addresses this topic by comparing the geomechanical behavior of an iron ore tailings and a silty sand with similar particle size distribution. The experimental plan comprised triaxial compression tests consolidated under isotropic and anisotropic conditions complemented by shear wave velocity and permeability measurements. Scanning electron micrographs with chemical analysis by energy dispersive spectroscopy spectrums, and morphological analysis to analyze shape descriptors supported the data interpretation. The results show some similarity in the stress–strain curves, but also several differences in compressibility, stiffness, and compaction density which are probably dependent on the distinct particles’ density. Moreover, the iron tailings showed higher brittleness and slightly higher critical state friction angle and instability stress ratio, which may be associated with less rounded particles. The results were analyzed on the scope of other soils within the same grain size range, in terms of critical state line, elastic stiffness, and hydraulic conductivity, confirming the wide variability and highlighting the need to pursue further work on these man-made materials.
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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 | 0.001 | 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".