Uranium mineral-resources: the current state and perspectives for development. Review
Bibliographic record
Abstract
The article covers current state, prospects of development, priority directions of reproduction and expansion of the uranium mineral resource base of the Russian nuclear industry. The state of the uranium mining industry in Russia and for individual uranium mining enterprises as Priargun Production Mining and Chemical Corporation PJSC, Hiagda JSC and Dalur JSC is characterized. The main plans for the development of uranium mining enterprises and existing problems are reflected. Within the framework of the problem, promising provinces, regions and specific exploration objects are presented, which require forecast-thematic, prognostic-mineragenic and prospecting works. According to the basic scenario of the development of the world nuclear power industry, uranium mining by 2030 should increase by 1,5 times. Production at operating mines will decline, and the planned new mines will only be able to compensate for the outgoing capacity. It is planned an additional 30 thousand tons of uranium per year will be extracted at new promising mines. Despite the depressive uranium market, uranium mining in 2016 reached 62 thousand tons – a historic maximum since 1988. The total uranium resource is more than sufficient to ensure the long-term needs of nuclear industry. In-situ underground leaching becomes main method of uranium extraction since 2010. Rosatom has acquired a Canadian public company Uranium One in 2010 in order to secure long-term uranium supply for Russian nuclear fuel cycle chain. Rosatom has consolidated on its basis high-performance uranium assets in Kazakhstan and in other countries. Uranium One has increased annual production almost 5 times during the last 8 years and became a fourth global U producer.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".