Risk assessment of potentially toxic elements in soil surrounding the Golesh ferronickel mine, Kosovo
Bibliographic record
Abstract
Purpose. The objective of this study was to assess the risk of potentially toxic elements in soil samples surrounding ferronickel mines in the Golesh massif, Republic of Kosovo. Methods. In total, 14 potentially toxic elements (Al, As, Cd, Co, Cr, Cu, Fe, Li, Mg, Mn, Ni, Pb, V and Zn) were investigated. Basic statistics, Pearson correlation, Principal Component Analysis (PCA), and Pollution indices (CF, PLI, Igeo, and EF) were used to explain better the data on metal concentrations in the soil samples. Findings. Five groups of elements were identified by PCA, based on their geogenic or anthropogenic origin. The contamination factor for nickel ranged from 6.9 to 166, with a mean value of 65.17. Cobalt and magnesium also had high mean values of contamination factor: 10.38 and 9.76, respectively. The PLIsite for 14 locations were highly polluted with metals (PLI > 4), and the PLIzone of the whole territory investigated was 3.5. The mean value of Igeo for nickel was 5.44, for cobalt (2.79) and for magnesium (2.7). The mean value of enrichment factor (EF) for nickel, cobalt and magnesium was 233.7, 35.26 and 19.16, respectively. Originality.Soil samples were collected from 30 different locations in accordance with the soil sampling protocol. The samples were sent for further analysis at the ACME, Ltd. laboratory in Vancouver, Canada. The soil samples were digested with aqua regia, and the content of 14 chemical elements was determined using inductively coupled plasma-mass spectrometry (ICP-MS). Practical implications. Based on statistical analysis and pollution indices, we concluded that most soil samples were highly polluted with Ni, Co, and Mg, resulting from the ferronickel and magnesite mines located in the region under investigation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".