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Record W2004015965 · doi:10.5194/acpd-11-387-2011

Organic condensation – a vital link connecting aerosol formation to climate forcing

2011· article· en· W2004015965 on OpenAlexafffundabout
Ilona Riipinen, Jeffrey R. Pierce, Taina Yli‐Juuti, Tuomo Nieminen, S. A. K. Häkkinen, Mikael Ehn, Heikki Junninen, Katrianne Lehtipalo, Tuukka Petäjä, Jay G. Slowik, Rachel Chang, N. C. Shantz, Jonathan P. D. Abbatt, W. R. Leaitch, Veli‐Matti Kerminen, D. R. Worsnop, Spyros Ν. Pandis, Neil M. Donahue, Markku Kulmala

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change CanadaUniversity of TorontoDalhousie University
FundersMaj ja Tor Nesslingin SäätiöEuropean CommissionCanadian Foundation for Climate and Atmospheric SciencesCamille and Henry Dreyfus Foundation
KeywordsAerosolCloud condensation nucleiForcing (mathematics)Particle (ecology)Atmospheric sciencesEnvironmental scienceCondensationNucleationVisibilityRadiative forcingAir quality indexClimate changeClimatologyMeteorologyChemistryGeographyPhysicsGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Atmospheric aerosol particles influence global climate as well as impair air quality through their effects on atmospheric visibility and human health. Ultrafine (<100 nm) particles often dominate aerosol numbers, and nucleation of atmospheric vapors is an important source of these particles. To have climatic relevance, however, the freshly-nucleated particles need to grow in size. We combine observations from two continental sites (Egbert, Canada and Hyytiälä, Finland) to show that condensation of organic vapors is a crucial factor governing the lifetimes and climatic importance of the smallest atmospheric particles. We demonstrate that state-of-the-science organic gas-particle partitioning models fail to reproduce the observations, and propose a modeling approach that is consistent with the measurements. We demonstrate the large sensitivity of climatic forcing of atmospheric aerosols to these interactions between organic vapors and the smallest atmospheric nanoparticles – highlighting the need for representing this process in global climate 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.207
Teacher spread0.183 · 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

Citations6
Published2011
Admission routes3
Has abstractyes

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