Methodological bases of mineral resource potential assessment: international and Russian experience
Bibliographic record
Abstract
Тhe paper considers the main approaches to the valuation of mineral deposits. The valuation of mineral resources is widely used in countries with developed mining industry, such as the USA, Canada, Australia, etc. Monitoring the value of mineral assets allows you to track current changes in their structure and serves as a basis for the fair withdrawal of mining rent. The methods of financial and economic evaluation of mineral deposits are based on the standard methodology for investment projects assessment. The most widely used is the net present value method, which is used only for the estimation of commercial reserves. The resource assessment can be carried out using comparative methods and be used to improve the infor-mativeness of the assessment. The paper reviews the methods used to access the mineral resource potential of Russian regions, forms of statistical observation, and standards of the Russian Society of Appraisers. Contemporary Russian legislation in the field of mineral raw material valuation is based on international experience, where the main valuation method of mineral assets is the method of net present value. With the approval in 2017 of the statistical form "Information on the current market value of mineral reserves”, official annual data on the value of mineral raw materials in the subsurface appeared in Russia for the first time. The methodology for assessing the mineral resource potential of the region should include such stages as ranking mineral deposits according to their investment attractiveness, evaluating selected deposits with approved reserves using the net present value method with determining the budget efficiency of projects, and evaluating the gross potential value of resources of promising mineral resource objects.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".