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Record W4411176038 · doi:10.1029/2024gl113365

Characteristics of Ice Nucleating Particles From the Long‐Range Transport of Saharan Dust

2025· article· en· W4411176038 on OpenAlexafffund
Ryan Patnaude, Christina S. McCluskey, Greg Roberts, Paul J. DeMott, Thomas C. J. Hill, Greg M. McFarquhar, Pavlos Kollias, Keyvan Ranjbar, Mengistu Wolde, Sonia M. Kreidenweis

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaU.S. Department of EnergyNational Science Foundation
KeywordsRange (aeronautics)Ice nucleusMineral dustGeologyAtmospheric sciencesEnvironmental scienceNucleationMeteorologyAerosolMaterials scienceGeographyPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract Transported mineral dust in a Saharan air layer (SAL) contains active ice‐nucleating particles (INPs) that may be transported across the Atlantic Ocean and subsequently seed clouds in the Caribbean and the Americas. During an aircraft campaign around Houston and the western U.S. Gulf Coast, a widespread SAL advected into the sampling region allowing for measurement of the ice‐nucleating ability of SD following long‐range transport. Results showed that the mean INP concentrations were 3–4.5 times higher than non‐Saharan dust (nSD), but only at temperatures <−21°C. Active surface site densities were also enhanced in the SD, exceeding the mean for nSD by over an order of magnitude at temperatures <−21°C. These INP measurements confirmed that SD remains an active INP even after >8,000 km westward transport across the Atlantic Ocean.

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 score0.004
Threshold uncertainty score0.008

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)0.0010.000

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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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