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Record W2324201540 · doi:10.3808/jei.201200216

Distribution of Polychlorinated Biphenyls (PCBs) and Toxic Equivalency of Dioxin-Like PCB Congeners in Rural Soils of Beijing, China

2012· article· en· W2324201540 on OpenAlexaboutno aff
Shuai Wu

Bibliographic record

VenueJournal of Environmental Informatics · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesProgram for New Century Excellent Talents in University
KeywordsBeijingCongenerSoil waterEnvironmental scienceEnvironmental chemistryChinaPollutionPersistent organic pollutantEnvironmental engineeringContaminationGeographyChemistryEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

The soil quality in rural area may associate with the health of local residents and food safety. This study aimed to investigate the spatial and vertical distribution of PCBs in rural soils of Beijing, and the source identification of PCBs and toxic equivalency calculation of dioxin-like PCB congeners were conducted. The mean of total PCB concentrations in Beijing rural soils was 11.01 ng g-1. The congener profiles were predominated by lowly chlorinated congeners such as Di-CBs, Tri-CBs and Tetra-CBs. No significant difference of PCBs concentrations was observed between plain and mountain areas, and PCBs were slightly accumulated in the surface soils of Beijing rural area. Long-range transport of volatile PCB congeners was also an important source in addition to local sources of PCBs from electronic products. Toxic equivalency concentrations of dioxin-like PCB congeners met Canadian soil quality guidelines for agricultural soils, indicating a lower risk of PCBs to human health.

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)0.000
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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Same venueJournal of Environmental InformaticsSame topicToxic Organic Pollutants ImpactFrench-language works237,207