A black‐box automated approach to calibrate numerical simulations and optimize cover design: Application to a flow control layer constructed on an experimental waste rock pile
Bibliographic record
Abstract
Abstract Mining operations often produce large volumes of waste rock to access economically valuable mineralized zones. Waste rock is usually stored in surface piles, the construction and reclamation of which represent a challenge for the industry. A flow control layer (FCL) made of crushed waste rock or sand and constructed on top of each waste rock bench could contribute to control water infiltration, thus improving waste rock pile stability and limiting contamination. An experimental waste rock pile was built and instrumented at the Tio mine (Rio Tinto Fer et Titane, Canada) to evaluate the performance of an FCL in field conditions. Large infiltration tests and rainfall monitoring were carried out, and measured outflow and water contents were used to calibrate numerical simulations. However, data were noisy and sometimes incomplete, and the models were difficult to calibrate. A new automated calibration approach was therefore proposed. An algorithm was developed to automate the numerical simulation calibration, using a black‐box method that involves solving an optimization problem on a function without an analytic form. The approach was applied on measurements obtained from large‐scale infiltration tests and validated using 2 yr of field monitoring data. Finally, the automated approach was adapted to optimize the design of the FCL, and an optimal design (material properties and layer thickness) was recommended based on local climate conditions. The proposed automated method could contribute to reduce the bias induced by manual calibration and allows for rapid multivariable calibration and optimization for a broad spectrum of mine waste cover system applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".