{"id":"W3082840893","doi":"10.5194/acp-20-10111-2020","title":"Impact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approach","year":2020,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Compute Canada; National Center for Atmospheric Research; National Science Foundation","keywords":"Drizzle; Seeding; Turbulence; Cloud condensation nuclei; Condensation; Liquid water content; Environmental science; Atmospheric sciences; Turbulence kinetic energy; Meteorology; Mechanics; Aerosol; Cloud computing; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003751493,0.000665486,0.0006255523,0.000457103,0.0006658603,0.0009435748,0.0008929827,0.001230889,0.001170941],"category_scores_gemma":[0.0006453702,0.0002202997,0.0006360048,0.0004280175,0.0004217789,0.0004107663,0.0004822954,0.0006261861,0.0001088156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091524,"about_ca_system_score_gemma":0.0006464805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03291116,"about_ca_topic_score_gemma":0.01766172,"domain_scores_codex":[0.9998459,0.00003349396,0.000008991766,0.00002676357,0.00003847972,0.00004639473],"domain_scores_gemma":[0.999375,0.000358297,0.0000644633,0.00004249024,0.00009261138,0.00006721371],"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":[0.0001327606,0.0004376525,0.01418489,0.00009326455,0.00004692226,0.001210705,0.00006400047,0.9668882,0.01145602,0.001488066,0.0003457395,0.003651822],"study_design_scores_gemma":[0.00002346925,0.00009032845,0.001938087,0.000003998948,0.00001312478,0.00004872719,0.00004682806,0.9930969,0.00432756,0.0001524004,0.000247774,0.00001077935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898648,0.0001820997,0.004880139,0.0001050405,0.00003332445,0.00008794773,0.0003212929,0.00007166396,0.004453745],"genre_scores_gemma":[0.9957124,0.00008377176,0.003469324,0.00001750611,0.000006930113,0.00001702167,0.0001102401,0.00000897458,0.0005738815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03291116,"threshold_uncertainty_score":0.06543916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02353011517514384,"score_gpt":0.2716030058716143,"score_spread":0.2480728906964705,"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."}}