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Record W2075350508 · doi:10.1029/2007jd009525

Carbonaceous aerosol composition over various regions of China during 2006

2008· article· en· W2075350508 on OpenAlexaff
X. Y. Zhang, Y. Q. Wang, X. C. Zhang, W. Guo, Sunling Gong

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsAerosolEnvironmental scienceTotal organic carbonEnvironmental chemistryBiomass burningSeasonalityAtmospheric sciencesChineChinaChemistryMeteorologyGeographyGeologyEcology

Abstract

fetched live from OpenAlex

The elemental (EC) and organic carbon (OC) fractions of ambient aerosols were determined by thermo‐chemical analysis of 24‐h samples collected during 2006 at 18 stations in China located at various rural, urban and remote locations. The annual mean concentration levels are found to be 0.35 ± 0.01 μ g EC m −3 and 3.0 ± 0.21 μ g OC m −3 for the remote background sites; 3.6 ± 0.93 μ g EC m −3 and 16.1 ± 5.2 μ g OC m −3 for the regional sites; and 11.2 ± 2.0 μ g EC m −3 and 33.1 ± 9.6 μ g OC m −3 for the urban sites, respectively. At rural sites representing regionally dispersed aerosols, levels are comparable to other locations in Asia. At all sites, both EC and OC show a consistent seasonal variation with a peak in winter, dropping in spring, reaching a minimum in summer and then increasing in autumn. The ratio of OC to EC is on the order of 3 for the urban locations, but can reach as high as 6 at the rural sites. This may partly be due to the open biomass burning in field of rural area, but may also indicate the presence of a regional background of secondary organic carbon (SOC) in China. These high ratios of OC/EC complicate the assessment of the climatic impacts of carbonaceous aerosols in China, as optical scattering from the high OC concentrations may lead to a cooling effect that counteracts the possible warming caused by EC optical absorption.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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.

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

Citations242
Published2008
Admission routes1
Has abstractyes

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