Physical and Numerical Modeling of a Flow Control Layer Made with a Sludge and Slag Mixture for Use in Waste Rock Pile Reclamation
Bibliographic record
Abstract
The reclamation of waste rock piles (WRPs) is complex, requiring adaptation of existing mine site reclamation techniques. An alternative approach has been developed for waste rock piles reclamation which involves installing finer materials on the top of waste rock piles. These finer layers (flow control layers—FCLs) redirect water flowing inside the pile toward its slope and limits water infiltration into reactive waste rocks. In the context of sustainable development, a mixture material made with sludge and slag can be used as an FCL in the reclamation of a waste rock pile. To assess the effectiveness of this material, a physical model was used and instrumented with sensors for monitoring volumetric water content and suction and equipped with the following components: (1) a rain simulator; and (2) drains that allow the recovery of water that infiltrates through the system. The physical model was tested with various cover layer thicknesses, inclinations, and precipitation rates. Investigation results showed that the water infiltration across the system was very low, leading to the conclusion that the sludge and slug mixture performed well as a flow control layer in the reclamation of waste rock piles.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".