Environmental Assessment of Artisanal Gold Mining on Soils of a Community within Southwestern, Nigeria
Bibliographic record
Abstract
Abstract Artisanal gold mining a means of livelihood comes with its public health challenge in most Sub-Saharan African countries. The study therefore evaluates extent of artisanal gold mining pollution on the environment and public health in communities within Ilesha Osun Nigeria. The study was field and laboratory based. Thirty–five (35) top soil samples (0-20cm) were randomly collected around Ijana, Itagunmodi, Epe, Igbadae and Ifewara communities. Samples were analyzed using an Agilent 700 series Inductively Coupled Plasma (ICP) for the 35 element suite in Acme Laboratory, Canada. Statistical evaluation was done using geo–accumulation index, enrichment Factor, contamination factor, pollution load index, contamination degree and nemerow pollution Index. The heavy metal pollution level in soils was assessed using potential ecological risk index. Human health risk was assessed using hazard index, carcinogenic risk index and non–carcinogenic risk index. Results of metal content in the soils revealed wide variation in heavy metal concentration. The mean metal content of soil when compared with crustal average was higher with decreasing order Cr > Bi > Sb > Sn > W > As > Mo > Ag > Cd. The results of the contamination indices carried out showed that Bi, Sc and V contributed greatly to very high contamination of the soils. Health risk assessment revealed that the children are more prone to non–carcinogenic diseases than adults in the area. However, carcinogenic health risk showed that adults are prone to this type of health risk through oral ingestion of Cr. This study therefore uncovered that heavy metals extending over large areas may pose great threat to the environmental media.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".