ROŞIA MONTANĂ-ABRUD AURIFEROUS AREA AT CROSSROADS: INTENSIVE MINING EXPLOITATION VERSUS TOURISTIC CAPITALIZATION
Bibliographic record
Abstract
Roşia Montană-Abrud auriferous area at crossroads: intensive mining exploitation versus touristic capitalization. Roşia Montană-Abrud is a mining area located in the North-Eastern side of the Metaliferi (Metalliferous) Mountains, part of the Apuseni Mountains, and holds the biggest gold and silver ores deposit in Europe. It also belongs to the Gold (or Auriferous) Quadrilateral, an important metalliferous region which covers around 2500 km2 between Baia de Arieş-Zlatna-Săcărâmb-Ţebea. Roşia Montană, the oldest mining locality in Romania, enhanced even more its fame after the apparition of Roşia Montană Gold Corporation (RMGC), a Romanian-Canadian mining company which, according to its claims, intends to exploit (even if it has-legally-only an exploration licence) the existing 300 tons of gold and 1600 tons of silver, by usind cyanide. In order to store the approximately 200 000 tons of residual cyanides, the company intends to built a huge tailings pond (about 180 m height and 800 ha ) in Corna Valley. By comparison, the Bozânta tailings pond near Baia Mare, which broke down in 2000, had only about 90 ha. On the other side, the opponents of the RMGC project aims to protect both natural and cultural heritage, which embed a richness of archaeological, historical and architectural objectivs, particularly. Also, there have been found arguments for the inscription of the area in UNESCO World Heritage Tentative List. This study aims also to highlight the main types of tourism feasible for the mentioned area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".