The geostatistical evaluation of coal parameters in Seam H, Malinau area, Indonesia
Bibliographic record
Abstract
Geostatistical modelling of coal quality parameters has not been widely adopted in Indonesia. Geostatistical interpolation of non-assay data allows the mine planner to estimate quality parameters regarding mining. The foresight can then be used for more timely and accurate seam blending protocols. This paper discusses the modelling and the spatial variability of three coal parameters, namely calorific value, ash content, and sulphur content, of a coal seam in Malinau, East Kalimantan, Indonesia. It is concluded that the use of the kriging procedure is strongly influenced by the amount of drill-hole data and their areal distribution. The results are more realistic when more drill-hole data are available and the distribution is even. It is thus worthwhile to consider the inclusion of geostatistical methods into mine planning as they can identify the errors associated with large reserve tonnage and quality estimates when used appropriately. The use of these methods could be a good opportunity to enhance mine planning in Indonesia.
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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 | 0.001 | 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".