Mineral reserves classification and reporting
Bibliographic record
Abstract
Mineral Resource Reporting is a standardized framework that ensures transparency, consistency, and comparability when disclosing mineral resource data to the public. Its primary goal is to protect investor interests by providing accurate, reliable information essential for investment decisions, regulatory compliance, and guiding exploration and development activities. The framework is particularly vital for stakeholders—including investors, regulatory bodies, mining companies, and the public—offering a consistent basis for evaluating resource estimates, reserve classifications, and project financing decisions within the mining industry. Over the past fifty years, several countries have implemented mineral reporting standards, and international efforts have focused on harmonizing these standards. The Committee for Mineral Reserves International Reporting Standards (CRIRSCO), formed in 1994, leads these global efforts, with representatives from Australia, Canada, Europe, South Africa, and the USA. Despite the alignment of these regions, China, Russia, and India use distinct classification schemes, which vary slightly from the internationally accepted standards. Key reporting standards derived from CRIRSCO’s guidelines include the JORC Code (Australasia), NI 43-101 (Canada), and the SAMREC Code (South Africa), which help standardize reporting in their respective regions. In mineral reporting, resources and reserves are categorized based on the level of geological confidence. Mineral resources are classified as Inferred, Indicated, and Measured, while mineral reserves are classified as Probable and Proven. The reporting process involves collecting and validating exploration data, conducting resource estimation using geological modeling, and ensuring compliance with regulatory standards through a Competent Person’s (CP) certification. The industry faces challenges such as data inconsistencies and evolving regulatory standards, but innovations like AI and machine learning are improving reporting accuracy and efficiency. Future trends are likely to see continuous updates to CRIRSCO and PERC standards, reflecting the industry's ongoing evolution.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".