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Record W3032323816 · doi:10.1126/sciadv.aay4945

Size-dependent influence of NO <sub>x</sub> on the growth rates of organic aerosol particles

2020· article· en· W3032323816 on OpenAlexaff
Chao Yan, Wei Nie, Alexander L. Vogel, Lubna Dada, Katrianne Lehtipalo, Dominik Stolzenburg, Robert Wagner, Matti Rissanen, Mao Xiao, Lauri Ahonen, Lukas Fischer, Clémence Rose, Federico Bianchi, Hamish Gordon, Mario Simon, Martin Heinritzi, Olga Garmаsh, Pontus Roldin, António Dias, Penglin Ye, Victoria Hofbauer, A. Amorim, Paulus S. Bauer, Anton Bergen, Anne-Kathrin Bernhammer, Martin Breitenlechner, Sophia Brilke, Angela Buchholz, Stephany Buenrostro Mazon, Manjula R. Canagaratna, Xuemeng Chen, Aijun Ding, Josef Dommen, Danielle C. Draper, Jonathan Duplissy, Carla Frege, Claudia Heyn, R. Guida, J. Hakala, Liine Heikkinen, C. R. Hoyle, Tuija Jokinen, Juha Kangasluoma, J. Kirkby, Jenni Kontkanen, Andreas Kürten, Michael J. Lawler, Huajun Mai, Serge Mathot, Roy L. Mauldin, Ugo Molteni, Leonid Nichman, Tuomo Nieminen, J. B. Nowak, Andrea Ojdanic, Antti Onnela, Aki Pajunoja, Tuukka Petäjä, Felix Piel, Lauriane L. J. Quéléver, Nina Sarnela, Simon Schallhart, Kamalika Sengupta, Mikko Sipilä, António Tomé, Jasmin Tröstl, Olli Väisänen, Andrea C. Wagner, Arttu Ylisirniö, Qiaozhi Zha, Urs Baltensperger, K. S. Carslaw, Joachim Curtius, Richard C. Flagan, Armin Hansel, Ilona Riipinen, James N. Smith, Annele Virtanen, Paul M. Winkler, Neil M. Donahue, Veli‐Matti Kerminen, Markku Kulmala, Mikael Ehn, Douglas R. Worsnop

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsNational Research Council Canada
FundersNatural Environment Research CouncilJiangsu Collaborative Innovation Center for Climate ChangeSvenska Forskningsrådet FormasHorizon 2020 Framework ProgrammeAcademy of FinlandNational Natural Science Foundation of ChinaSight Research UKNational Science FoundationRoyal SocietySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of EnergyAustrian Science FundAgence Nationale de la RechercheVetenskapsrådet
KeywordsAerosolVolatility (finance)Particle-size distributionParticle sizeGrowth rateChemical physicsEnvironmental scienceChemical engineeringMaterials scienceEnvironmental chemistryChemistryStatistical physicsEconometricsPhysicsMathematicsPhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

NO x is important for particle growth as it can participate in HOM formation and alter the HOM volatility distribution.

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 score0.003
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations140
Published2020
Admission routes1
Has abstractyes

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