Distributional Coefficients and Enrichment Studies of Potentially Toxic Heavy Metals in Soils Around Itakpe Iron-Ore Mine, North Central Nigeria
Bibliographic record
Abstract
Soil samples were collected randomly but uniformly distributed around Itakpe iron-ore mines in both dry and wet seasons. Surface soils were collected from 0cm to 10cm using stainless steel augers and located using Global Positioning System (GPS). Soil samples were air-dried, sieved through 500um mesh and 1.0g digested, evaporated and analysed using Atomic Absorption Spectrometer (AAS).Five (5) geo-environmental indices were used to quantitatively evaluate the degree of soil contamination due to iron ore mining. The anthropogenic factor (AF) for both seasons revealed that all heavy metals have greater than 50% AF except for Cd in the dry season. The geo accumulation index (Igeo) for both seasons showed background concentration to unpolluted for Cu and Zn while Fe, Ni, Cdand Pb recorded moderately to very highly polluted. The pollution index (Eri), showed tiny hazard level for all the heavy metals in dry season and in wet season, Cd and Ni recorded strong hazard level while tiny hazard level were observed for Cu and Pb. The ecological pollution index for the area is strong (RI=323.25). Dry and wet season enrichment factor (EF) revealed background concentration for all the heavy metals except Fe with EF> 40 (extremely high enrichment). While contamination factor (CF) was very high for Fe in both seasons, Cu and Ni recorded considerable to very high contamination in dry season. The wet season also revealed considerable contamination for Ni and Cd; moderate to considerable contamination for Cu, Zn and Pb. The sites in both seasons have experienced various degrees of deterioration but more significant in wet season. Based on these indices, the soils around Itakpe iron-ore area has suffered significant degrees of contaminations with respect to Fe, Ni, Cd and Pb.
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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".