{"id":"W2768089565","doi":"10.1175/jas-d-17-0123.1","title":"Turbulence Effects of Collision Efficiency and Broadening of Droplet Size Distribution in Cumulus Clouds","year":2017,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Turbulence; Drizzle; Coalescence (physics); Collision; Physics; Mechanics; RADIUS; Stokes number; Turbulence kinetic energy; Computational physics; Dissipation; K-epsilon turbulence model; Clear-air turbulence; Meteorology; Statistical physics; Thermodynamics; Reynolds number; 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":[0.0007703169,0.00007348395,0.0002006502,0.000003564546,0.0001394803,0.00003972599,0.0006930478,0.00003522804,0.00000141829],"category_scores_gemma":[0.001328653,0.00004443635,0.00005209093,0.0002778725,0.0004501419,0.000251958,0.0001089448,0.0001217155,1.946049e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006369442,"about_ca_system_score_gemma":0.00004525909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003384012,"about_ca_topic_score_gemma":0.000004830576,"domain_scores_codex":[0.9990423,0.00004164319,0.0003412969,0.00007638231,0.0003491812,0.0001492468],"domain_scores_gemma":[0.9990659,0.000304661,0.0003398256,0.0001984425,0.00005473992,0.00003644322],"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":[0.00002797328,0.00006496785,0.0905069,0.000146409,0.00001699177,0.00001223237,0.0008148635,0.7939007,0.1105522,0.0002572511,0.00006172962,0.003637739],"study_design_scores_gemma":[0.0003585591,0.0001157895,0.1618338,0.0003155645,0.00001284177,0.00002268418,0.00005942242,0.8198163,0.01706202,0.0003381016,0.000008811355,0.00005610812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975871,0.0006737023,0.0008150648,0.00006919729,0.0007072896,0.00007008454,0.000001390819,0.000004003113,0.00007219402],"genre_scores_gemma":[0.9979379,0.0001571878,0.00186926,0.000003087481,0.00002088133,6.046196e-7,1.888525e-8,0.000003611408,0.000007495211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09349021,"threshold_uncertainty_score":0.1812062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005702677391836743,"score_gpt":0.2341987443940041,"score_spread":0.2284960670021674,"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."}}