A new hazard assessment workflow to assess soil contamination from large and artisanal scale gold mining
Bibliographic record
Abstract
Abstract Gold mining activities are undertaken both at large and artisanal scale, often resulting in serious ‘collateral’ environmental issues, including environmental pollution and hazard to human and ecosystem health. Furthermore, some of these activities are poorly regulated, which can produce long-lasting damage to the environment and local livelihoods. The aim of this study was to identify a new workflow model to discriminate anthropogenic versus geogenic enrichment in soils of gold mining regions. The Kedougou region (Senegal, West Africa) was used as a case study. Ninety-four soil samples (76 topsoils and 18 bottom soils) were collected over an area of 6,742 km 2 and analysed for 53 chemical elements. Robust spatial mapping, compositional and geostatistical models were employed to evaluate sources and elemental footprint associated with geology and mining activities. Multivariate approaches highlighted anomalies in arsenic (As) and mercury (Hg) distribution in several areas. However, further interpretation with enrichment factor (EFs) and index of geoaccumulation (IGeo) emphasised high contamination levels in areas approximately coinciding with the ones where artisanal and small scale mining (ASGM) activities occur, and robust compositional contamination index (RCCI) isolated potentially harmful elements (PHE) contamination levels in very specific areas of the Kedougou mining region. The study underlined the importance of complementary approaches to identify anomalies and, more significantly, contamination by hazardous material. In particular, the analyses helped to identify discrete areas that would require to be surveyed in more detail to allow a comprehensive and thorough risk assessment, to investigate potential impacts to both human and ecosystem health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".