Current state of industry practice in mineral resource estimation and classification
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
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.
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 | 0.001 |
| 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