Role of fines on cohesive behavior of mine tailings inferred from critical shear stress
Bibliographic record
Abstract
The significance of fines on the cohesive behavior of mine tailings has been investigated by examining the incipient motion of the tailings. Sixteen laboratory experiments were performed in a Plexiglas laboratory annular column on re-constituted mine tailings under a 50 cm water cover. Re-suspension was produced by a Teflon stirrer and the velocity field in the column was characterized using a laser Doppler velocimeter (LDV). The pressure change in the boundary layer was also measured with a Preston tube. It was observed that the nondimensional critical shear stresses showed deviation from those of the noncohesive model results at a fines content greater than 50%–55%. An empirical relation that shows the relation between the boundary shear stress deviation and the percent fines in the tailings was proposed. Regression analysis of the experimental results showed that a power law relationship could reasonably be used to describe the relation between the measured nondimensional excess bed shear stress and the erosion rate. It is proposed that the value of β (the erosion rate constant) could be taken as 1 for mine tailings that show cohesive behavior.
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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) | 0.000 | 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) | 0.000 | 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 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".