Drill-hole spacing optimization for profit in grade control
Bibliographic record
Abstract
Reaching an informed decision about optimal drill-hole spacing (DHS) is an essential task in geostatistics that adds value to mining projects. The optimal DHS is sensitive to many factors, including inherent geologic characteristics of the deposit, mining and operational parameters or constraints, economic factors, the purpose of the mineral resource estimation, and the metric to be optimized. Final estimates at the grade control (GC) stage of mining are meant to maximize the correct classification of mineable volumes. When considering dedicated GC drilling, DHS optimization for profit balances the cost of estimation uncertainty and the cost of drilling. The drilling amount is optimal when drilling less would incur large estimation costs and drilling more would incur large drilling costs. We developed a DHS framework for regularly spaced drilling aimed at maximizing profit in GC. Each of the steps are described in detail, including sequential Gaussian simulations, resampling, estimation, transfer function customization, mineable limits definition, and final profit calculation. The DHS framework is demonstrated on a realistic data set, followed by a sensitivity analysis to relevant factors. This work establishes a conceptual foundation and provides practical details for developing DHS optimization for final estimates in mining operations with dedicated drilling systems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".