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Record W2120224853 · doi:10.4209/aaqr.2013.01.0032

Distribution of Polychlorinated Dibenzo-p-dioxins and Dibenzofurans in the Atmosphere of Beijing, China

2014· article· en· W2120224853 on OpenAlexaff
Zhiguang Zhou, Bin Zhao, Qi Li, Pengjun Xu, Yue Ren, Nan Li, Sen Zheng, Hu Zhao, Shuang Fan, Ting Zhang, Aimin Liu, Yeru Huang, Liguo Shen

Bibliographic record

VenueAerosol and Air Quality Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsBeijingCongenerSeasonalityEnvironmental scienceEnvironmental chemistryAtmosphere (unit)Polychlorinated Dibenzo-p-dioxinsPolychlorinated dibenzofuransSpatial distributionAtmospheric sciencesChemistryMeteorologyChinaGeographyGeology

Abstract

fetched live from OpenAlex

Air samples were collected from six locations during the periods of February, 2011–March, 2012 in Beijing city. Samples were analyzed using HRGC-HRMS according to HJ 77. 2-2008. The concentrations, congener profiles, seasonal variation, spatial distribution, and sources identified were investigated. The mass concentrations of 2,3,7,8-substituted PCDD/Fs is 1750 to 10380 fg/m3 with an average of 3670 fg/m3. The highest concentration was observed at site DS in February 2011 and the lowest was at site NS in July 2011. The TEQ concentrations of PCDD/Fs ranged from 35.0 to 751 fg I-TEQ/m3, with an average of 251 fg I-TEQ/m3. The dominant contributor to total TEQ value is 2,3,7,8-PeCDFs, with an average contribution of 40%. Low-chlorinated PCDDs were observed in all samples in winter. The concentration and profiles exhibit obvious seasonal variation. The PCDD/Fs at six sampling sites did not show obvious spatial variation. Seasonal variation of PCDD/F concentrations indicated that the emission sources may be different. Fossil fuel combustion and automobiles may be the main dioxins emission sources during the heating period, whereas, in non-heating period, the automobiles alone are the main emission sources.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.335
Teacher spread0.298 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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