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Record W2055911404 · doi:10.4236/jep.2011.210153

Sources of Polycyclic Aromatic Hydrocarbons in Street Dust from the Chang-Zhu-Tan Region, Hunan, China

2011· article· en· W2055911404 on OpenAlexaff
Yongzhen Long, Guoxiang Chi, Hairuo Qing, Qianhong Wu

Bibliographic record

VenueJournal of Environmental Protection · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Regina
FundersChina Geological Survey
KeywordsCoal combustion productsEnvironmental scienceCoalCombustionEnvironmental chemistrySmeltingRefineryChimney (locomotive)PetroleumEnvironmental engineeringWaste managementMining engineeringMetallurgyChemistryGeologyMaterials scienceEngineeringSmoke

Abstract

fetched live from OpenAlex

Street dusts collected from 20 sites as well as three special dust samples collected from chimney of coal-fired plant, smelter and refinery of nonferrous metals and automobile exhaust, respectively, in the Chang-Zhu-Tan (Changsha, Zhuzhou and Xiangtan) urban region, Hunan, China, in May to August 2009, were investigated for sources of polycylic aromatic hydrocarbons (PAHs). The ΣPAHs16 levels were in the range of 3515 - 24488 ng/g, with a mean of 8760 ng/g. The sources of PAH inputs to street dusts were determined by isomer ratios, principal components analysis and REE geochemical analysis. The isomer ratios suggested a rather uniform mixture of coal combustion and petroleum PAH sources. Factor analysis indicated that the main sources of 16 PAHs were coal combustion/vehicle exhaust and coking/ petroleum. Rare earth elements (REE) and Factor score analysis further indicated the possible dust sources were from background soil, coal or coking combustion, nonferrous metal factories, traffic exhaust.

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 score0.053
Threshold uncertainty score0.105

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.192
Teacher spread0.176 · 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
Published2011
Admission routes1
Has abstractyes

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