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Record W2587842698 · doi:10.1021/acs.est.6b06160

Molecular-Size-Separated Brown Carbon Absorption for Biomass-Burning Aerosol at Multiple Field Sites

2017· article· en· W2587842698 on OpenAlexafffundabout
Robert A. Di Lorenzo, R. A. Washenfelder, A. R. Attwood, Hongyu Guo, Lu Xu, N. L. Ng, Rodney J. Weber, Karsten Baumann, Eric S. Edgerton, Cora J. Young

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbsorbanceAerosolChemistrySize-exclusion chromatographyBiomass burningAbsorption (acoustics)Carbon fibersEnvironmental chemistryBiomass (ecology)Fraction (chemistry)Absorption spectroscopyAnalytical Chemistry (journal)ChromatographyOrganic chemistryMaterials scienceEcologyOptics

Abstract

fetched live from OpenAlex

Biomass burning is a known source of brown carbon aerosol in the atmosphere. We collected filter samples of biomass-burning emissions at three locations in Canada and the United States with transport times of 10 h to >3 days. We analyzed the samples with size-exclusion chromatography coupled to molecular absorbance spectroscopy to determine absorbance as a function of molecular size. The majority of absorption was due to molecules >500 Da, and these contributed an increasing fraction of absorption as the biomass-burning aerosol aged. This suggests that the smallest molecular weight fraction is more susceptible to processes that lead to reduced light absorption, while larger-molecular-weight species may represent recalcitrant brown carbon. We calculate that these large-molecular-weight species are composed of more than 20 carbons with as few as two oxygens and would be classified as extremely low volatility organic compounds (ELVOCs).

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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

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

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations145
Published2017
Admission routes3
Has abstractyes

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