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Record W2902949162 · doi:10.4314/jasem.v22i5.17

Health risk assessment model for lead contaminated soil in Bagega Community, Nigeria

2018· article· en· W2902949162 on OpenAlexaboutno aff
OC Alaba, Z. O. Opafunso, Grace Kisiwaa Agyei

Bibliographic record

VenueJournal of Applied Sciences and Environmental Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthRisk assessmentHealth riskIngestionInhalationContaminationHealth risk assessmentMedicineInhalation exposureEnvironmental scienceToxicologyBiology

Abstract

fetched live from OpenAlex

The study developed health risk assessment model for lead contaminated soil in Bagega community using United States Environmental Protection Agency (US EPA) and Canadian Standards Association (CAS) standard procedures. Questionnaires were used to investigate the background causes and exposure pathways of lead contaminated soil. Soil samples were collected at five different sites and cancer health risk values were estimated using equations proposed by US EPA. The results show that 84.0 % of the respondents agreed that the causes of lead poisoning in the study area were due to the activities of artisanal gold miners. The major exposure pathways to lead contaminated soil are ingestion, dermal contact and inhalation while the soil ingestion generates high cancer risk, dermal contact generates low cancer risk and that of inhalation was insignificant when compared with 1.00E-06 (mg/kg/day) WHO cancer risk standard. The mean cancer health risk value for combined exposure pathway is ranged from 1.49E-03 mg/kg/day to 5.99E-03 mg/kg/day. The study established that lead contaminated soil posed cancer health risk to the people of the study area.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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