Erosion characteristics and stochastic nature of bed shear stress in underwater mine tailings
Bibliographic record
Abstract
The erosion of mine tailings was investigated by examining the physical processes during the initiation of motion of the tailings. Erosion experiments were conducted on mine tailings samples and natural soils in a Plexiglas laboratory annular column under 50 cm water cover. Resuspension was introduced with a Teflon stirrer and the bed shear stress was estimated from the measured near-bed velocity field and the pressure change in the boundary layer. Two modes of initiation of motion of cohesive mine tailings that showed cohesive behaviour was noticed: pitting erosion and line erosion and the modes of initiation of motion changed mainly with percentage of fines. At incipient motion of the tailings that showed cohesive behaviour, the pore water pressure distribution showed a relative sudden peak and a decline when the aggregated tailings burst. A four order of magnitude difference was observed between the undrained shear strength and critical shear stress for surface erosion of the tailings. The stochastic nature of the bed shear stress was explained by the Rayleigh distribution that provides an approach for correcting the critical shear stress estimated from the near-bed velocity. This correction is necessary to achieve a conservative estimate of the critical shear stress for design purposes.
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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) | 0.000 | 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".