Environmental Geochemistry of Mine Tailings Soils in the Artisanal Gold Mining District of Betare -Oya, Cameroon
Bibliographic record
Abstract
Changes in trace element concentrations in mine tailings (dry and wet) were investigated in the gold mining area of Bétaré-Oya, Eastern Cameroon. Forty-one surface sediment samples were analyzed using ICP-MS for heavy metals and pollution was assessed using Enrichment Ratio and Geo Accumulation Index (Igeo); using a sample from a remote area as control.Trace elements in mine tailings show significant increase compared to the background (control) values, with the exception of Sr and Nb. It is suggested that allochtonous deposition may account for Sr distribution. A strong correlation is observed between the lithophile elements; Y, Nb, Ce, La, and Pb. Their association with each other is also strong, i.e. Nb/Ce (.73) and La/Ce (.63). The close association of these elements in the samples may be an indication of the effect of resistant heavy minerals from felsic rocks (mainly granites and gneisses). Cu, Zn, Sr and Ba distribution is associated with mineralisation of sulphide-bearing minerals and clay formation from weathering of mica schist. Cu, Zn, As, W, Mo and Ag have been identified as potential pollutants. Compared to the Dutch soil quality guidelines, these elements are above the target values and below the intervention levels. Arsenic is considered to be the most threatening element, with regards to its potency. The distribution of As in this area appears to be controlled by mining activities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".