Pollution of coal mine soils: global reference concentrations of chemical elements
Bibliographic record
Abstract
Abstract Despite global efforts to phase out coal, the world’s coal production and consumption reached a record high in 2022. Even though soil pollution around collieries stands in the shadow of greenhouse gas emissions, the anthropogenic geochemical impact of coal mining will persist for decades to centuries after the coal phaseout. Soils are of paramount significance when assessing the pollution of mining sites. This analysis provides a reference dataset for evaluating soil transformation in coal minescapes. Identification, screening, eligibility check, and extraction of data from articles published in peer-reviewed journals between 2000 and 2022 yielded a comprehensive dataset on the chemical composition of 13,925 soil samples from 55 mined coal fields in 32 countries of Eurasia, Africa, Australia, and the Americas. These carefully handpicked records allowed the calculation of mean concentrations for 41 chemical elements, alongside total organic carbon and a total of 15 rare-earth elements. The resulting dataset is of both fundamental geochemical and policy-relevant significance. The maximum enrichment of contaminated soils with As, Bi, Hg, Sb, and Se reveals the role of coals as the source of highly coalphile elements. Remediation guidelines can benefit from the dataset, e.g., for arsenic whose world average contents fall below the standards of Canada, Russia, and the USA. Regional soil quality criteria may incorporate these figures to update threshold levels for mining sites. Finally, for the areas of discovered coal reserves, the question “to mine, or not to mine” can be answered with higher certainty owing to the predicted levels of pollutant burden.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".