Geo-accumulation Index, Enrichment Factor and Quantification of Contamination of Heavy Metals in Soil Receiving Cassava Mill Effluents in A Rural Community in the Niger Delta Region of Nigeria
Bibliographic record
Abstract
This study investigated enrichment factor, geo-accumulation index and quantification of contamination of heavy metals in cassava mill effluents contaminated soil by smallholder cassava processors in a rural community in the Niger Delta region of Nigeria. Data used for the environmental risk assessment is from secondary sources. The assessment was carried out under two background scenarios viz: geometric (BGM) and median mean (BMM). 50% of mean detected individual metals was used as mean data for location that the metal was not detected. Assessment was carried out following well established protocol. Results showed that enrichment factor (EF), geo-accumulation index (Igeo), improved Nemerow Index (INI), Metal enrichment index (MEI) and quantification of contamination (QoC) in soil heavy metals (viz: Fe, Cr, Zn, Cu, Co, Ni, Mn, Pb and Cd) receiving cassava mill effluents in a rural community of the Niger Delta region of Nigeria revealed un-contamination to moderately contamination for Igeo, NMI, background rank to significant enrichment for EF, no enrichment to moderate enrichment for MEI, and positive values of quantification of contamination is an indication of pollution/contamination due to anthropogenic sources. The study further showed that cassava mill effluents are contributing to soil heavy metal contamination in study area.
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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 | 0.000 | 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 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".