High Arsenic Enrichment in Water and Soils from Sambayourou Watershed – Burkina Faso (West Africa)
Bibliographic record
Abstract
Sambayourou is one of the main tributary of Mouhoun River in southwest Burkina Faso. Its watershed is part of area affected by mining operations from Poura gold mine in 80s. Investigations on surface water, ground-water and soil from Sambayourou watershed reveal that enormous volume of mine wastes from Poura old gold mine is causing acid mine drainage (AMD). This latter is characterized by a red-brick color, a low pH (2.9) and high contents of arsenic and heavy metals: arsenic (753 ppm), iron (4948 ppm), zinc (51 ppm), copper (38 ppm), cobalt (7 ppm) and lead (4 ppm). The oxidation and acidification of the mine wastes have also resulted in the pollution of some groundwater with concentrations of arsenic and lead beyond acceptable standards. Arsenic is the most polluting element of surface water and ground-water. Concerning ground-water contamination, arsenic come from both mine wastes and host rocks. To assess soil contamination, geo-accumulation indexes (Igeo) and enrichment factor (EF) are used. The use of the index of geo-accumulation is based on seven descriptive classes for increasing geo-accumulation index values. The different values of enrichment factor are divided into five groups corresponding to five categories of contamination. According to geo-accumulation values, the soil in Sambayourou watershed is strongly contaminated by arsenic. This situation is confirmed by enrichment factor which indicates a very high enrichment in arsenic. The very high enrichment in arsenic can derive from erosion of host rocks of ores mineralization which contain sulfide minerals as arsenopyrite (FeAsS) and/or from pedogenesis processes. However, mine wastes are assumed to be the main sources of arsenic contamination of soil in Sambayourou watershed.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".