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Record W2895189904 · doi:10.20897/ejosdr/3912

Levels and Risk Assessment of Polychlorinated Biphenyls (PCBS) in Soils from Informal E-Waste Recycling Sites in Cameroun

2018· article· en· W2895189904 on OpenAlexaboutno aff
Romaric Emmanuel Ouabo, Mary B. Ogundiran, Babafemi A. Babalola

Bibliographic record

VenueEuropean Journal of Sustainable Development Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
FundersPan African UniversityAfrican Union
KeywordsEnvironmental chemistrySoil waterIngestionEnvironmental scienceHealth riskHuman healthSoil testRisk assessmentChemistryEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

This study assessed the levels and human health risk of polychlorinated biphenyls (PCBs) in soils of e-waste recycling sites in Douala, Cameroun. Surface soil samples from these sites were collected and analyzed by Gas Chromatography- Electron Capture Detector to quantify the levels of 30 PCBs (including 10 dioxin-like PCBs). The investigated 30 PCBs were detected in all the soil samples. The mean and standard deviation of the total PCBs in Makea, Ngodi and New Bell recycling sites were 32.1±4.48, 31.9±0.10 and 72.8±13.5 ng/g, respectively. Between 26-46% of the Ʃ30 PCB concentrations were comprised of the dioxin-like PCB congeners. The toxic equivalent (TEQ) values of 10 dioxin-like PCBs were lower than the Canadian soil quality guidelines of dioxin (4 pg TEQ g−1). Human health risk through ingestion, dermal contact, and inhalation was lower than the values of cancer risk (10−6) indicating low adverse effects of PCBs in the recycling sites.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.325
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Sustainable Development ResearchSame topicRecycling and Waste Management TechniquesFrench-language works237,207