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Composition of Size-Resolved Aged Boreal Fire Aerosols: Brown Carbon, Biomass Burning Tracers, and Reduced Nitrogen

2018· article· en· W2787764197 on OpenAlexafffund
Robert A. Di Lorenzo, Bryan K. Place, Trevor C. VandenBoer, Cora J. Young

Bibliographic record

VenueACS Earth and Space Chemistry · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaBanting Research Foundation
KeywordsLevoglucosanChemistryAerosolDimethylamineEnvironmental chemistryDiethylamineNitrogenAmmoniumBiomass burningOrganic chemistry

Abstract

fetched live from OpenAlex

Aerosols that were size-resolved into 13 fractions between 10 nm and 18 μm were collected from an aged boreal forest wildfire plume in July 2013. Samples were extracted into water and analyzed for molecular-size-resolved brown carbon (BrC), biomass burning (BB) markers, reduced nitrogen compounds, and elemental composition. Absorption of BrC was primarily in fine-mode aerosols and dominated by high-molecular-weight compounds (>500 Da). The molecular size distribution of BrC was conserved across aerosol sizes, with a decrease in the importance of large molecules in smaller aerosols. The aerosol-size-resolved composition of BrC absorption was different than those of the two BB markers, non-sea-salt potassium and levoglucosan, suggesting that they may not be suitable for identifying BB BrC in aged plumes. Strong correlations were observed between BrC and the reduced nitrogen compounds ammonium, dimethylamine, and diethylamine. In aerosols with high BrC and reduced nitrogen, there was a strong cationic excess. These observations could be caused by (i) uptake of ammonium and alkylamines to form stable salts with organic acids or (ii) reactive uptake to form imines or enamines that were hydrolyzed during the BrC extraction process.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.774

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 studies0.0000.000
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designBench or experimental
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

Citations56
Published2018
Admission routes2
Has abstractyes

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