Nickel mine planning application to support smelter demand fulfilment: a case study of gag nickel mine
Bibliographic record
Abstract
Abstract Gag Nickel is a mining project in West Papua developed by Antam, with a total resource of approximately 300 million wet metric tons. In the central block, mining activities are carried out to meet the demand target for the nickel smelter. There are two types of specifications, saprolite and limonite ores, each with specific grade requirement based on sales purchase contract. The aim of this study was to fulfill the long-term target from 2024 to 2028 through integrated mine planning to generate production scheduling plan based on agreed contract of around 6 million wet metric tons ore per year. The quantitative method was implemented to gather primary data, including the 6-meter-high bench and material classification based on specific quality of elements-compounds, and to use the secondary data such as reserve block model to create a reverse model. The challenges of this study included different ore specifications of each location of the hill, and constraints on limited stockyard location to support differentiation between two types of products. Based on the simulations performed, production and sales targets could be achieved according to predetermined product specifications, amounting to 6 million wet metric tons of saprolite and limonite with fluctuating processes during the period. From the scheduling results, the smelter supply composition ratio between saprolite and limonite was between 1.37 and 1.85. After the implementation above, Gag Nickel can conserve resources by optimizing reserves of both limonite and saprolite for optimum utilization.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".