Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review
Bibliographic record
Abstract
This review article considers the following aspects: naturally distributed chemical elements and their enrichments, and the increased occurrence of PTEs due to anthropogenic and urban activities, as well as due to the geochemical uniqueness of certain geochemical landscapes, depending on the lithological environment. The review article is the result of many years of successful cooperation between the Geological Survey of Slovenia and the Faculty of Natural Sciences in Skopje, Ss. Cyril and Methodius University in Skopje, as well as several other institutions from North Macedonia, Russia and Romania but, also, through the voluntary and enthusiastic work of Prof. Trajče Stafilov’s PhD and Master’s students. To create the Geochemical Atlas, the territory of North Macedonia was covered with 995 sampling locations, but 16 separate areas with soil contamination were additionally sampled. The total sum of all collected soil samples was 3983 from 2449 different sampling sites in the period from 2006 to 2017. The analyses were performed at the Institute of Chemistry, Faculty of Natural Sciences in Skopje, at the Ss. Cyril and Methodius University in Skopje, North Macedonia, at the Research Institute for Analytical Instrumentation (ICIA), Cluj-Napoca, Romania, at the Joint Institute for Nuclear Research in Dubna, Moscow Region, Russia, and at Acme Labs in Vancouver, Canada. The sum of all analysed soil samples in all four mentioned laboratories was 7991 from 2006 to 2017. Using advanced mathematical methods such as multivariate statistical methods (HCA, FA, PCA) and artificial neural networks–multilayer perceptron (ANN-MP), predictions were made about the concentrations of potentially toxic elements (PTEs) and their distribution in real space. In less than two decades (2007–2023) of fruitful collaboration, a large number of scientific works have been published: 188 scientific publications, 8 geochemical atlases and 23 chapters in monographs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".