{"id":"W4413536119","doi":"10.1175/jas-d-24-0269.1","title":"Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning, and Parcel Models","year":2025,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"TRACER; Convection; Environmental science; Cloud computing; Atmospheric sciences; Meteorology; Field (mathematics); Geology; Physics; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006461039,0.0002515199,0.0002877684,0.000346129,0.0001407675,0.0002974067,0.0002438317,0.0002192917,0.0001798907],"category_scores_gemma":[0.0007759313,0.0001593422,0.0003283776,0.0002410974,0.0001566815,0.0004951382,0.0002490762,0.0002009898,0.00004652939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002472015,"about_ca_system_score_gemma":0.000222955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007518707,"about_ca_topic_score_gemma":0.00952177,"domain_scores_codex":[0.9998559,0.00004015261,0.000007308347,0.00004670008,0.00003085491,0.00001913326],"domain_scores_gemma":[0.9995713,0.0002118761,0.00006847418,0.00006509942,0.00004934155,0.00003385885],"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":[0.000702473,0.000378018,0.7079182,0.00006021043,0.000286286,0.0001552734,0.000136608,0.1168519,0.1277079,0.0002900049,0.0002775516,0.04523558],"study_design_scores_gemma":[0.00002178621,0.0001661635,0.4319793,0.00000533415,0.00004971626,0.00003738845,0.0000501694,0.5457807,0.0215812,0.0001196426,0.0001899615,0.00001866356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965003,0.00002685814,0.003110894,0.000009776169,0.000001867381,0.000006409266,0.0001084957,0.00005970206,0.0001757065],"genre_scores_gemma":[0.9980532,0.000009428594,0.001754691,0.000004030755,0.0000017268,0.00000538899,0.0001298491,0.000006385428,0.00003523022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007518707,"threshold_uncertainty_score":0.01494992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165685074513978,"score_gpt":0.2111390851765163,"score_spread":0.1894822344313765,"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."}}