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High viscosity and two phases observed over a range of relative humidities in biomass burning organic aerosol from Canadian wildfires

2024· preprint· en· W4404505872 on OpenAlexafffundabout
Nealan G. A. Gerrebos, Julia Zaks, Florence K. A. Gregson, Max Walton-Raaby, Harrison Meeres, Ieva Zigg, Wesley F. Zandberg, Allan K. Bertram

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsOkanagan University CollegeUniversity of WaterlooStemcell TechnologiesUniversity of British Columbia, Okanagan CampusThompson Rivers UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerosolBiomass burningBiomass (ecology)Range (aeronautics)Atmospheric sciencesViscosityEnvironmental scienceMeteorologyMaterials scienceGeographyAgronomyPhysicsComposite materialBiology

Abstract

fetched live from OpenAlex

Biomass burning organic aerosol (BBOA) is a major contributor to organic aerosol in the atmosphere. The impacts of BBOA on climate and health depend strongly on their physicochemical properties, including viscosity and phase behaviour (number and types of phases); these properties are not yet fully characterized. We collected BBOA field samples during the 2021 British Columbia wildfire season to constrain the viscosity and phase behaviour at a range of relative humidities, and compared them to previous studies on BBOA. Particles from all samples exhibited two-phased behaviour with a polar hydrophilic phase and a non-polar hydrophobic phase. We used the poke-flow viscosity technique to estimate the viscosity of the particles. Both phases of the BBOA had viscosities >108 Pa s at relative humidities up to 50%. Such high viscosities correspond to mixing times within 200 nm BBOA particles of >5 h. Two phases and high viscosity have implications for how BBOA should be treated in atmospheric models.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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