MétaCan
Menu
Back to cohort
Record W1895555186 · doi:10.5194/acp-16-9255-2016

Theoretical investigation of mixing in warm clouds – Part 2: Homogeneousmixing

2016· article· en· W1895555186 on OpenAlexaff
Mark Pinsky, А. Хаин, Alexei Korolev, Leehi Magaritz‐Ronen

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
FundersOffice of ScienceIsrael Science FoundationU.S. Department of Energy
KeywordsMixing (physics)HomogeneousSupersaturationDispersityConstant (computer programming)EvaporationChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract. Evolution of monodisperse and polydisperse droplet size distributions (DSD) during homogeneous mixing is analyzed. Time-dependent universal analytical expressions for supersaturation and liquid water content are derived. For an initial monodisperse DSD, these quantities are shown to depend on a sole non-dimensional parameter. The evolution of moments and moment-related functions in the course of homogeneous evaporation of polydisperse DSD is analyzed using a parcel model.It is shown that the classic conceptual scheme, according to which homogeneous mixing leads to a decrease in droplet mass at constant droplet concentration, is valid only in cases of monodisperse or initially very narrow polydisperse DSD. In cases of wide polydisperse DSD, mixing and successive evaporation lead to a decrease of both mass and concentration, so the characteristic droplet sizes remain nearly constant. As this feature is typically associated with inhomogeneous mixing, we conclude that in cases of an initially wide DSD at cloud top, homogeneous mixing is nearly indistinguishable from inhomogeneous mixing.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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 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

Citations42
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAtmospheric chemistry and physicsSame topicAtmospheric aerosols and cloudsFrench-language works237,207