{"id":"W2786204985","doi":"10.5194/acp-18-7251-2018","title":"Bridging the condensation–collision size gap: a direct numerical simulation of continuous droplet growth in turbulent clouds","year":2018,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing Municipal Science and Technology Commission; Environment and Climate Change Canada; Western Canada Research Grid; Compute Canada; National Science Foundation","keywords":"Turbulence; Condensation; Collision; Direct numerical simulation; Mechanics; Adiabatic process; Cloud condensation nuclei; Physics; Computer simulation; Statistical physics; Work (physics); Meteorology; Aerosol; Thermodynamics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001020188,0.0001209602,0.0001712484,1.785649e-7,0.00004650254,0.00001831023,0.00008931965,0.00005538843,0.0000405715],"category_scores_gemma":[0.00006442088,0.0001083283,0.00003487885,0.0002306052,0.00009887241,0.00006000196,0.00001892132,0.0001125817,0.000003842205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004872504,"about_ca_system_score_gemma":0.00001064853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002359124,"about_ca_topic_score_gemma":6.781414e-7,"domain_scores_codex":[0.9993523,0.00001771603,0.0002080714,0.0001406817,0.0001253007,0.0001559521],"domain_scores_gemma":[0.9994758,0.0002233816,0.00004021257,0.0001673041,0.00005850486,0.00003480694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000500376,0.0000876164,0.006298321,0.0001597799,0.00005872024,0.000005638761,0.00146625,0.8811066,0.08686884,0.000178579,0.0001194046,0.02360016],"study_design_scores_gemma":[0.000279443,0.00001358706,0.002327867,0.00002878845,0.00001360221,0.000002587933,0.0000263503,0.9668571,0.02975295,0.0004883041,0.00009168317,0.0001177817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879115,0.00008816625,0.009618901,0.00002579818,0.00005199059,0.00009069751,0.000004157342,0.00005758882,0.00215116],"genre_scores_gemma":[0.9987692,0.00002255911,0.0008877385,0.00002267454,0.0001924573,0.000008371268,0.000004296413,0.0000181199,0.00007463135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08575041,"threshold_uncertainty_score":0.4417501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006811949474717241,"score_gpt":0.2207643875634963,"score_spread":0.213952438088779,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}