A multi-step approach to long-term open-pit production planning
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
The objective of this paper is to develop, verify, and present a multi–step methodology for three interrelated key components of open–pit mine planning: controlled optimal phase–design, characterisation of selective mining–units, and long–term production scheduling optimisation. A hybrid solution methodology for open–pit phase–design using integer programming and a local search heuristic is presented. Next, a hierarchical clustering approach with size and shape control, which aggregates blocks into minable polygons constrained within the pushback boundaries, is presented; and finally, a mixed integer linear programming mathematical model, which uses the generated pushbacks and aggregates as the planning units to provide near–optimal practical life–of–mine schedules, is introduced. In addition, the model inherently solves the cut–off grade optimisation problem. Two case–studies of real–size deposits are presented to illustrate practicality of the developed methodologies, and also to compare the results against industrial conventional practices to assess validity, performance, strengths, and limitations of the developed methodologies.
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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