Cost analysis of material handling systems in open pit mining: Case study on an iron ore prefeasibility study
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
Selection of the optimal material handling system is one of the most significant decisions to be made in mineral industries. Rapid economic changes and technological improvements make cost analysis a complicated process. On the other hand, current low commodity prices have put a greater emphasis on cost reduction and process optimization to ensure viability of mining projects. In this article, two material handling systems, a semimobile in-pit crusher and conveyor systems (IPCC) and traditional truck and shovel systems (TS), are compared through the cost analysis of an iron ore prefeasibility study. Furthermore, robustness of the design parameters is evaluated through a sensitivity analysis to determine the relative importance of project parameters. Finally, risks associated with uncertain design parameters affecting cost analysis are assessed through Monte Carlo simulation. The results indicated that IPCC is more cost effective than TS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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