MétaCan
Menu
Back to cohort
Record W2577437211 · doi:10.1039/c6em00636a

Quantifying the equilibrium partitioning of substituted polycyclic aromatic hydrocarbons in aerosols and clouds using COSMOtherm

2017· article· en· W2577437211 on OpenAlexafffund
Boluwatife Awonaike, Chen Wang, Kai‐Uwe Goss, Frank Wania

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsThe Scarborough HospitalUniversity of TorontoToronto Public Health
FundersEnvironment and Climate Change CanadaUniversity of Cambridge
KeywordsEnvironmental chemistryChemistryPolycyclic aromatic hydrocarbonEnvironmental science

Abstract

fetched live from OpenAlex

Functional groups attached to polycyclic aromatic hydrocarbons (PAHs) can significantly modify the environmental fate of the parent compound. Equilibrium partition coefficients, which are essential for describing the environmental phase distribution of a compound, are largely unavailable for substituted PAHs (SPAHs). Here, COSMOtherm, a software based on quantum-chemical calculations is used to estimate the atmospherically relevant partition coefficients between the gas phase, the aqueous bulk phase, the water surface and the water insoluble organic matter phase, as well as the salting-out coefficients, for naphthalene, anthracene, phenanthrene, benz(a)anthracene, benzo(a)pyrene and dibenz(a,h)anthracene and 62 of their substituted counterparts. They serve as input parameters for the calculation of equilibrium phase distribution of these compounds in aerosols and clouds. Our results, which were compared with available experimental data, show that the effect of salts, the adsorption to the water surface and the dissolution in a bulk aqueous phase can be safely neglected when estimating the gas-particle partitioning of SPAHs in aerosols. However, for small PAHs with more than one polar functional group the aqueous phase can be the dominant reservoir in a cloud.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.639

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 studies0.0010.002
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.265
Teacher spread0.231 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science Processes & ImpactsSame topicAtmospheric chemistry and aerosolsFrench-language works237,207