Measuring the onset of mine tailings erosion
Bibliographic record
Abstract
The present study gives details of a methodology for estimating the critical shear stress for erosion of mine tailings and other naturally occurring cohesive sediments. Erosion of a cohesive sediments bed occurs when the critical shear stress is exceeded to break the interparticle bond. Experiments were conducted in a 30 cm diameter laboratory column and calibrated using laser Doppler anemometry. The results showed that the erosion pattern of mine tailings particles was similar to those of fine-grained cohesive sediments. A power-law relation of the form E = α[(τ – τcr)/τcr]nis suggested for mine tailings, where E is the erosion rate, α is a coefficient, τ is the shear stress, τcris the critical shear stress, and n is an exponent. The computed values of α, n, and τcrin the power-law equation were found to be comparable to values derived from experiments in a rotating circular flume. The derived expression for rate of erosion may be incorporated in resuspension and transport models for fine mine tailings of a similar nature.Key words: mine tailings, laser Doppler velocimetry, wall shear stresses, critical shear stress for erosion, erosion – shear stress relationship.
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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".