The concept of critical minerals as a mean of stimulate the development of subsoil use in Ukraine
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
Developed countries use a list of critical minerals to identify and stimulate priority areas for the mineral resource base development. The article provides an overview and main features of the terms “critical minerals”, “critical elements”, “critical commodities”, “critical materials”, “critical elements”. The criticality parameters (indicators) are supply risk and economic importance, production concentration, changing the size of the market and geological resources, market dynamics (changing prices). Various methods for assessment the criticality of minerals are analyzed in the article. Lists of critical minerals USA, Australia, EU, Canada are compared. The amount and names of critical minerals vary from region to region and may change over time. An analogue of “critical minerals” was “strategic minerals”, which existed in Ukrainian law until 2016. This term was inherited from the USSR and implies minerals, which are mainly used in the military industry. Following the example of developed countries, the legitimization and application of the concept and list of critical minerals can be a mechanism to stimulate the development of certain areas of geological exploration and mining. For this, it is necessary to determine the list of critical minerals, adapting the existing world advanced methods. Obviously, this requires special research, including marketing studies, but we can preliminarily assume which minerals will make the list and which are candidate minerals. Most likely, the list of critical minerals for Ukraine will differ significantly from the list of strategic minerals. Providing of such list in law would give certain advantages and preferences (in particular tax) to companies that perform geological exploration and mining of listed minerals in Ukraine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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