Drillhole spacing determination with value of information
Bibliographic record
Abstract
Different quantities of information are available at various stages of the development of a mining project. Consequential decisions are made given the data available at the time. Geological uncertainty due to sparse data presents economic risk. The collection of additional information reduces geological uncertainty leading to a better technical decision and greater value. Subjectivity in the choice of data collection scheme may lead to sub-optimal outcomes. The value of information (VOI) allows a decision-maker to quantify the future value data could provide before collecting it. Evaluating many future configurations over a range of data spacings identifies the optimal outcome given the value metric. The optimal data spacing represents the balance between the cost of uncertainty and the cost of information. A framework for establishing VOI in a mining context is proposed. A geostatistical “resample and resimulate” approach is adopted: the resampling of simulated realizations provides access to virtually any future data configuration. The difference in value generated with future information and the current information is the VOI. The methodology and techniques developed in this paper are applied to a synthetic example and an operating mine case study. The case study encompasses VOI principles, data spacing, engineering design parameters, and economic factors.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".