Stochastic numerical simulations of long term unsaturated flow in waste rock piles
Bibliographic record
Abstract
The authors present a numerical modelling study of unsaturated water flow in waste rock piles using selected realizations of stochastically distributed hydraulic properties (moisture content and hydraulic conductivity functions). Stochastic properties are used to represent spatial variability in the field. The simulations were performed to better understand the long term hydrogeological behaviour of waste rock piles, to help select the construction sequence, and to assess in situ groundwater monitoring methods. The results show that the spatial correlation of hydraulic properties directly affects the moisture distribution within the pile, which in some cases creates local preferential flow paths along the direction of the strongest correlation of hydraulic properties; for instance locally enhanced horizontal preferential flow is observed when hydraulic properties are strongly correlated along the horizontal axis. However, larger-scale seepage remains dominantly vertical in all cases. Simulations also show the influence of hydraulic property variability (i.e. standard deviations) on seepage along flow paths. RÉSUMÉ Les auteurs présentent une étude numérique sur l’écoulement non saturé de l’eau dans des haldes à stériles, en utilisant des réalisations particulières avec une distribution stochastique des propriétés hydriques (courbes de rétention d’eau et fonctions de conductivité hydraulique). La distribution stochastique est utilisée pour simuler la variabilité
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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 | 0.002 |
| 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) | 0.003 | 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".