Mine production scheduling for poly-metallic mineral deposits: extension to multiple processes
Bibliographic record
Abstract
Mine planning focuses on solving a series of decision-making problems; namely, determination of productions rates, ore-waste discrimination and block sequencing. These problems are currently solved in a sequential way leading to sub-optimality. In this paper, a new two-stage mine production scheduling is proposed for poly-metallic deposits. Using a marginal cut-off and conventional block sequencing approach, a sub-optimal plan is firstly generated. This plan is then submitted to the second stage to discriminate ore-waste and sequence blocks concurrently using a meta-heuristics. To improve the results in the first stage, new solutions are generated using two configuration mechanisms: 1) randomly selected blocks from the list containing block to be re-classified are re-identified with a probability; 2) randomly selected blocks are swapped from one period to the other without violating access constraint. To test the proposed technique, two case studies were conducted. The results showed that the approach could be effectively used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".