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Record W2129325965 · doi:10.5194/acp-10-7945-2010

Volume nucleation rates for homogeneous freezing in supercooled water microdroplets: results from a combined experimental and modelling approach

2010· article· en· W2129325965 on OpenAlexafffund
Michael E. Earle, Thomas Kühn, Alexei F. Khalizov, J. J. Sloan

Bibliographic record

VenueAtmospheric chemistry and physics · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of WaterlooEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercoolingNucleationIce nucleusCirrusAerosolEvaporationClassical nucleation theoryThermodynamicsChemistryVolume (thermodynamics)Materials scienceAtmospheric sciencesGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract. Temperature-dependent volume nucleation rate coefficients for supercooled water droplets, JV(T), are derived from infrared extinction measurements in a cryogenic laminar aerosol flow tube using a microphysical model. The model inverts water and ice aerosol size distributions retrieved from experimental extinction spectra by considering the evolution of a measured initial droplet distribution via homogeneous nucleation and the exchange of vapour-phase water along a well-defined temperature profile. Experiment and model results are reported for supercooled water droplets with mean radii of 1.0, 1.7, and 2.9 μm. Values of mass accommodation coefficients for evaporation of water droplets and vapour deposition on ice particles are also determined from the model simulations. The coefficient for ice deposition was found to be 0.031 ± 0.001, while that for water evaporation was 0.054 ± 0.012. Results are considered in terms of the applicability of classical nucleation theory to the freezing of micrometre-sized droplets in cirrus clouds, with implications for the parameterization of homogeneous ice nucleation in numerical 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 designBench or experimental
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

Citations66
Published2010
Admission routes2
Has abstractyes

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