{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001006131,0.0002868368,0.0003746678,4.344326e-8,0.0001454709,0.00004070068,0.0001340277,0.00008139422,0.0002192516],"category_scores_gemma":[0.00002578428,0.0002580531,0.00006848302,0.0004368541,0.0001827447,0.0001916434,0.0001355944,0.0002257781,0.000002317704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008814568,"about_ca_system_score_gemma":0.00002024943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001426647,"about_ca_topic_score_gemma":0.000001801367,"domain_scores_codex":[0.9985611,0.00005295864,0.0002833558,0.0005752419,0.0002436851,0.0002836598],"domain_scores_gemma":[0.9993053,0.00008497143,0.0001341985,0.0002095884,0.00001294694,0.0002529587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001380147,0.0009008874,0.6431855,0.00007706594,0.0000485644,0.0001605785,0.00679986,0.3327096,0.009239204,0.000002828505,0.0000123136,0.006725583],"study_design_scores_gemma":[0.0008235545,0.0005384276,0.0247578,0.00001938275,0.00005440211,0.00008195738,0.002305113,0.9704837,0.0004760815,0.00003860604,0.000006483001,0.0004144726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958143,0.00002664579,0.003126645,0.000008295778,0.00001583762,0.0002384245,0.000006904153,0.00002726776,0.0007357054],"genre_scores_gemma":[0.9958223,0.000008393252,0.00388629,0.00005035901,0.0001759451,0.000008101938,0.000003932077,0.00002570054,0.00001896494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6377741,"threshold_uncertainty_score":0.9999872,"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."}}