Current state of industry practice in mineral resource estimation and classification
Bibliographic record
Abstract
A review of 175 recent Australasian Joint Ore Reserves Committee (JORC), National Instrument 43‐101, and U.S. Securities and Exchange Commission technical reports was conducted to study prevailing practices in mineral resource estimation (MRE), mineral resource classification (MRC), and capping of extreme values workflows in the mining industry. The goal is to discover trends in current practices and examine differences between reporting jurisdictions and deposit types. Ordinary kriging is the predominant MRE method, but inverse distance weighting remains prevalent. Drill hole spacing (DHS) and search neighborhood are the most common criteria used for MRC, while statistical metrics such as kriging variance, slope of regression, and confidence intervals are rarely used. MRC method selection depends on deposit type, commodity type, drilling pattern, variogram range, and nugget effect. JORC reports often use DHS for MRC, while National Instrument 43-101 reports show more diversity. A proposed data-driven decision tree classifier predicts the most commonly used MRE, MRC, and capping strategies with accuracies of 82.6%, 83.6%, and 84.7%, respectively. This model is not intended to replace a practitioner’s method choice but allows them to quickly assess what others have considered for similar deposits. Note that we are careful in this work to avoid judgments of the “best” workflow: our goal is to highlight what is being done in the industry.
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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 | 0.078 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".