Heavy metals soil pollution state in relation to potential future mining activities in the Roşia Montană area
Bibliographic record
Abstract
The aim of this paper is to establish the abundance of heavy metals in the soils affected by the past Rosia Montana gold and silver ore mining, and in currently unaffected soils that will be impacted by the proposed Rosia Montana project that foresees the expansion of the ore exploitation and a new processing facility. The soil cover of the Rosia Montana area consists of five soil types: Eutricambosols, Districambosols, Regosols, Lithosols, and Aluviosols. The first two types are prevalent; they cover 73.83% of the total researched surface (1,646 ha). In the soils from the areas where mining activities have been carried out, the total content of the heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) vary from the region's pedogeochemical background level up to the alert threshold for heavy metals pollution set down in the Order of the Ministry of Waters, Forests, and Environment Protection no. 756/1997. The analysis of soils from and surrounding the existing ore processing facilities shows that the heavy metals contents in few cases is above the intervention threshold, for copper, lead and zinc. The soils generally have low heavy metals contents and the values are at the region's pedogeochemical background level. The barren rocks, generally, have low heavy metals contents, close to the clark values. Taking all this into account, as well as the technology that the Canadian company intends to apply, there is a low probability that a significant heavy metals pollution of the soils left un-stripped would occur due to the proposed project.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".