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Record W4214716916 · doi:10.1038/s41561-021-00891-1

Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols

2022· article· en· W4214716916 on OpenAlexafffund
Vaios Moschos, K. Džepina, Deepika Bhattu, Houssni Lamkaddam, Roberto Casotto, Kaspar R. Daellenbach, Francesco Canonaco, Pragati Rai, Wenche Aas, Silvia Becagli, Giulia Calzolai, Konstantinos Eleftheriadis, Claire E. Moffett, Jürgen Schnelle‐Kreis, Mirko Severi, Sangeeta Sharma, Henrik Skov, Mika Vestenius, Wendy Zhang, Hannele Hakola, Heidi Hellén, Lin Huang, Jean‐Luc Jaffrezo, Andreas Maßling, Jakob Klenø Nøjgaard, Tuukka Petäjä, Olga Popovicheva, Rebecca J. Sheesley, Rita Traversi, Karl Espen Yttri, Julia Schmale, Andrê S. H. Prévôt, Urs Baltensperger, Imad El Haddad

Bibliographic record

VenueNature Geoscience · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersHorizon 2020 Framework ProgrammeEnvironment and Climate Change CanadaStaatssekretariat für Bildung, Forschung und InnovationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAgence Nationale de la RechercheEuropean Commission
KeywordsEnvironmental scienceAtmospheric sciencesNatural (archaeology)ClimatologyAbundance (ecology)ArcticThe arcticOceanographyGeologyEcology

Abstract

fetched live from OpenAlex

Aerosols play an important yet uncertain role in modulating the radiation balance of the sensitive Arctic atmosphere. Organic aerosol is one of the most abundant, yet least understood, fractions of the Arctic aerosol mass. Here we use data from eight observatories that represent the entire Arctic to reveal the annual cycles in anthropogenic and biogenic sources of organic aerosol. We show that during winter, the organic aerosol in the Arctic is dominated by anthropogenic emissions, mainly from Eurasia, which consist of both direct combustion emissions and long-range transported, aged pollution. In summer, the decreasing anthropogenic pollution is replaced by natural emissions. These include marine secondary, biogenic secondary and primary biological emissions, which have the potential to be important to Arctic climate by modifying the cloud condensation nuclei properties and acting as ice-nucleating particles. Their source strength or atmospheric processing is sensitive to nutrient availability, solar radiation, temperature and snow cover. Our results provide a comprehensive understanding of the current pan-Arctic organic aerosol, which can be used to support modelling efforts that aim to quantify the climate impacts of emissions in this sensitive region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations75
Published2022
Admission routes2
Has abstractyes

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