Shaft Location Selection Based on Case-based Reasoning Cost Estimation Model
Bibliographic record
Abstract
In the mining industry, the selection of the location of a given piece of infrastructure is one of the most critical decision-making problems; some examples of infrastructure in the mine are the concentrator plant, the workshops, the waste dump, the tailing pond, the warehouse, the shaft, and others.Among all these, the shaft is one of the most expensive infrastructures in the lifetime of the underground mine.The principal function of a shaft is to transport ore, materials utilities, and the staff from the mine to the surface and vice versa, in addition is often the sole access to the underground operations.Given that the shaft location significantly affects the profitability and underground operations at a mine, its location is a key consideration in the mine design process.Selecting the shaft location is a complex process influenced by various factors, the primary ones being the positions and shapes of the orebodies, ore tonnage, rock characteristics, sinking method, mining equipment, and presence of water.The cost of excavating and transporting the ore, which depends on a complex combination of these factors, serves as the principal metric to evaluate the shaft location selection.Additionally, due to the high level of uncertainty around some of the problem's parameters, the selection of the shaft location can also be seen as a highrisky decision-making process.In this research study, a technically feasible polygon is initially defined for shaft localization, then it is discretized on the surface.For each discrete pattern a shaft sinking cost is calculated using a robust cost estimation model, and with the operational cost, the total cost associated to this discrete pattern is obtained.The best location for the shaft will be the grid cell with the minimum total cost.Given that there are many parameters are uncertain in this localization problem, a Monte-Carlo scheme is applied to evaluate the associated risks.The proposed methodology is tested through a case study.It provides a framework to facilitate the selection of the shaft location while considering the inherent uncertainties associated with some of the project parameters.I am grateful to my research supervisor
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".