Drill-hole spacing optimization for profit in grade control
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it