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Record W4223451023 · doi:10.1029/2022gl098951

Complexity in the Evolution, Composition, and Spectroscopy of Brown Carbon in Aircraft Measurements of Wildfire Plumes

2022· article· en· W4223451023 on OpenAlexafffund
R. A. Washenfelder, Lisa Azzarello, Katherine Ball, Steven S. Brown, Zachary C. J. Decker, Alessandro Franchin, Carley D. Fredrickson, Katherine Hayden, Christopher D. Holmes, A. M. Middlebrook, Brett B. Palm, R. Bradley Pierce, Derek J. Price, J. M. Roberts, Michael Robinson, Joel A. Thornton, Caroline C. Womack, Cora J. Young

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change CanadaYork University
FundersNational Oceanic and Atmospheric AdministrationNatural Sciences and Engineering Research Council of CanadaMitacsNational Aeronautics and Space Administration
KeywordsPlumeAbsorption (acoustics)Environmental scienceCarbon fibersAtmospheric sciencesAerosolAbsorption spectroscopyMeteorologyAnalytical Chemistry (journal)Environmental chemistryChemistryMaterials scienceGeologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Biomass burning is a major source of light‐absorbing organic aerosol (brown carbon), but its composition, chemical evolution, and lifetime are not well known. We measured water‐soluble brown carbon absorption from 310 to 500 nm on the National Oceanic and Atmospheric Administration Twin Otter aircraft during flights downwind of western United States wildfires in summer 2019. The sampling strategy was near‐Lagrangian and the plume ages spanned 0–5 hr. Trends in brown carbon mass absorption coefficient with plume age varied between flights, and did not show an exponential decay over these short time scales. The measured absorption spectra were smoothly varying, without identifiable contributions from individual chromophores with structured absorption. Using aerosol tracer ions and reference absorption spectra, the calculated contribution of 4‐nitrocatechol to total absorption was <22 ± 9% and <11 ± 5%, although spectral fitting showed that it may be as low as <1.1% and <0.6% at 365 and 405 nm, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

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 studies0.0000.000
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 head0.054
GPT teacher head0.287
Teacher spread0.232 · 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

Citations55
Published2022
Admission routes2
Has abstractyes

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