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Potentially Toxic Metals Enrichment in Soils of Ishiagu Mining Community, Southeastern Nigeria

2024· article· en· W4403229163 on OpenAlexaboutno aff
Bridget Ozibo-Igwe, O. L. Anike

Bibliographic record

VenueInternational Journal of Innovative Science and Research Technology (IJISRT) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterEnvironmental scienceHeavy metalsEnvironmental chemistryMining engineeringEnvironmental protectionGeologySoil scienceChemistry

Abstract

fetched live from OpenAlex

This Study was carried out to evaluate potentially toxic metals enrichment, pollution and potential ecological risk in the soils of Ishiagu. The concentrations of Cadmium (Cd), Chromium (Cr), Cobalt (Co), Copper (Cu), Lead (Pb), Manganese (Mn), Nickel (Ni), Zinc (Zn), Aluminum (Al) and Iron (Fe) in soil samples from the area were analyzed by Bureau Veritas Laboratory, Vancouver, Canada, using inductively-coupled plasma optic emission spectrometry (ICP-OES). Descriptive statistics, enrichment factor, pollution load and potential ecological risk indices were employed to analyze the data in order to understand the characteristics of potentially toxic metals in the study area. The potentially toxic metals statistic show the mean values (mg/kg-1 ) of Pb, Zn, Mn, Fe, Al, Cd, Cr, Ni, Co, and Cu to be 205.7, 162, 527, 35892, 11374, 1.3, 21.6, 15, 14.6 and 11.1 respectively. The soil enrichment varied considerably between mining and non-mining areas with mining area being extremely enriched with Pb, Zn and Cd, and significantly enriched with Co, Cu, Ni and Fe. Non- mining areas are minimally enriched with all investigated metals. The soils around the mines are polluted with Pb and Cd, where both metals also pose very high ecological risks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.386
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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