Delimitation of the Final Pit in Open Pit Mines Using the Pseudoflow Maximum Flow Algorithm: A Comparative Analysis of 1×5 and 1×9 Arcs
Bibliographic record
Abstract
The primary aim of this research was to delimitate the final pit in an open pit mine using the 1×5 and 1×9 arch methods of the pseudoflow maximum flow algorithm.To achieve this, Exploratory Data Analysis (EDA), economic, and geomechanical parameters were utilized.Various final pit scenarios were generated by varying the revenue factor.The analysis was conducted using Python 3.11 (Jupyter Notebook) and SGeMS V.3.0 software.The block model comprised 480,000 blocks, each measuring 10×10×10 meters, with a copper grade range from 0 to 1.41%.Specific parameters were employed, including a slope angle of 45°, a base copper price of 3.90 US$/lb, and smelting, extraction, and crushing-grinding costs of 0.40 US$/lb, 2.30, and 11.00 US$/ton, respectively.Twenty final pits were generated for each method, based on a revenue factor from 0.10 to 2.00.The results indicated that both methods are effective for final pit delineation, with the 1×5 method achieving an NPV of 17,855 MUS$ and a REM of 0.27, and the 1×9 method attaining an NPV of 18,456 MUS$ and a REM of 0.35.It was concluded that the 1×9 arch method is preferable as it yields a higher NPV.This study underscores the importance of methodological selection in the planning of open-pit mines, demonstrating that despite a higher REM, the 1×9 method significantly enhances the NPV, implying substantial economic benefits for the industry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".