A Computational Intelligent Algorithm for Surface Mine Layouts Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimized surface mine layouts are used to extract mineable reserves with minimum waste under economic geological, geotechnical, and property boundary constraints. Surface mine design and optimization algorithms are limited in dealing with the random field properties of these layouts, resulting in suboptimal results. Database changes also require complete rerun of these algorithms, resulting in long CPU times with no allowance for incorporating operating strategies. In this study, the authors develop a computational intelligent (CI) algorithm to solve these problems. The CI algorithm combines the stochastic models of ore reserves and commodity prices to generate economic block and target values. The error back-propagation algorithm is used to train feed-forward neural networks for block pattern recognition and partitioning based on the target values. The CI algorithm is used to optimize Section SBHP 860001 of a surface mine layout, and the results are compared with that from the 2-D Lerchs-Grossmann’s algorithm.
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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) | 0.000 | 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 it