THE EXPLOITATION OF THE TULGHEŞ-GRINŢIEŞ URANIUM DEPOSIT. BETWEEN BENEFITS AND CONTROVERSY
Bibliographic record
Abstract
The Exploitation of the Tulgheș-Grințieș Uranium Deposit. Between Benefits and Controversy. Romania is one of the few European states (alongside the Czech Republic, France, Germany, Ukraine) and one of the few in the world with uranium deposits (Canada, Australia, Niger, Namibia are others), mainly used in the energy sector. According to recent studies, the only currently exploited deposit (Crucea-Botușana, Suceava County) is nearly depleted (by 2019) and will be eventually shut down. For this reason, there are plans to open a new uranium mining facility in the Tulgheș-Grințieș area, where geological surveys have proven that the area holds the largest uranium deposit in the country. It will provide the necessary fuel for Cernavodă Nuclear Power Plant, for the two functional reactors, which have a total capacity of 706 MW each (producing roughly 18% of the country's electricity needs), as well as for units 3 and 4, not operational yet. The study at hand intends to emphasize several aspects regarding the exploitation possibilities for the uranium deposit from the two mineralized structures located in the fracture areas of the central Carpathian line, through which the crystalline overflows the Cretaceous Flysch. Furthermore, the environmental impact analysis as well as the long term safety and security of the population inhabiting the area will be of utmost importance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".