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Record W2150230102 · doi:10.1175/jas-d-11-012.1

Experimental Study of Collection Efficiencies between Submicron Aerosols and Cloud Droplets

2011· article· en· W2150230102 on OpenAlexaff
Luis A. Ladino, O. Stetzer, Bodo Hattendorf, Detlef Günther, Betty Croft, Ulrike Lohmann

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAerosolRADIUSEvaporationNucleationLaminar flowCloud chamberParticle (ecology)Relative humidityMaterials scienceIce nucleusCloud physicsMechanicsAnalytical Chemistry (journal)PhysicsChemistryMeteorologyThermodynamicsGeologyChromatographyNuclear physicsCloud computing

Abstract

fetched live from OpenAlex

Abstract Collection efficiency E experiments for aerosol particles scavenged by cloud droplets were carried out in the newly built Collision Ice Nucleation Chamber (CLINCH). Pure water droplets having radii between 12.8 and 20.0 μm were allowed to fall freely and to collide in a laminar flow with lithium metaborate particles having radii between 0.05 and 0.33 μm. At the bottom of the chamber, the droplets and the particles captured were collected using a cup impactor. The collected solution was analyzed for the scavenged aerosol mass by inductively coupled plasma mass spectrometry. Evaporation of droplets was taken into account since the relative humidity inside the chamber was below 100%, resulting in final theoretical droplet sizes between 4.2 and 17.6 μm. The resulting experimental measurements were compared with theoretical values to see their correlation. The authors found an experimental trend similar to theory, as well as the “Greenfield gap” at the particle radius of 0.24 μm (E = 0.038) for the smallest cloud droplet size investigated in this study. The experimental values of collection efficiency found herein agree with those from theory within one order of magnitude, similar to previous studies reported in the literature.

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)0.001
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 score0.026
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.028
GPT teacher head0.242
Teacher spread0.214 · 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

Citations59
Published2011
Admission routes1
Has abstractyes

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