Physical and numerical modelling of a dual-porosity fractured rock surrounding an in-pit uranium tailings management facility
Bibliographic record
Abstract
In-pit tailings management facilities (TMFs) have been used to dispose of uranium mine tailings in northern Saskatchewan. The pervious surround method can be employed to reduce groundwater flow through the tailings, and thereby moderate the rate of flux of radioactive and metal contamination that could enter the local groundwater via advection. A laboratory-scale analogue of an in-pit TMF was constructed to evaluate the impacts of a dual-porosity representation of the host rock on the effectiveness of the pervious surround concept in reducing peak concentrations and mass flux at a downgradient receptor. This work complemented the work of West et al. (2003) in which similar laboratory-scale experiments were conducted, simulating the host rock using the equivalent porous media and discrete fracture approaches. The experiments were adequately simulated using SWIFT II, a dual-porosity flow and transport model. Finally, to illustrate the impact of a dual-porosity representation of a fractured host rock on a contaminant plume from a TMF in the field, a field-scale scenario was modelled. The simulations illustrate the impact of diffusion into the host rock matrix on the simulated peak concentrations and the time for the peak concentrations to reach a downgradient receptor.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".