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Record W2170656341 · doi:10.1002/2015gl063693

Elemental composition of organic aerosol: The gap between ambient and laboratory measurements

2015· article· en· W2170656341 on OpenAlexaff
Qi Chen, Colette L. Heald, J. L. Jiménez, Manjula R. Canagaratna, Qi Zhang, Ling‐Yan He, Xiaofeng Huang, Pedro Campuzano‐Jost, Brett B. Palm, Laurent Poulain, Mikinori Kuwata, Scot T. Martin, Jonathan P. D. Abbatt, Alex K. Y. Lee, John Liggio

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
FundersBiological and Environmental ResearchOffice of ScienceNational Aeronautics and Space AdministrationU.S. Department of EnergyNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsAerosolCarbon fibersEnvironmental scienceTotal organic carbonEnvironmental chemistryAtmospheric sciencesChemistryMeteorologyMaterials scienceGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract A large data set including surface, aircraft, and laboratory observations of the atomic oxygen‐to‐carbon (O:C) and hydrogen‐to‐carbon (H:C) ratios of organic aerosol (OA) is synthesized and corrected using a recently reported method. The whole data set indicates a wide range of OA oxidation and a trajectory in the Van Krevelen diagram, characterized by a slope of −0.6, with variation across campaigns. We show that laboratory OA including both source and aged types explains some of the key differences in OA observed across different environments. However, the laboratory data typically fall below the mean line defined by ambient observations, and little laboratory data extend to the highest O:C ratios commonly observed in remote conditions. OA having both high O:C and high H:C are required to bridge the gaps. Aqueous‐phase oxidation may produce such OA, but experiments under realistic ambient conditions are needed to constrain the relative importance of this pathway.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.083
GPT teacher head0.298
Teacher spread0.215 · 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

Citations130
Published2015
Admission routes1
Has abstractyes

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