{"id":"W3015997515","doi":"10.5194/gmd-13-5737-2020","title":"A new parameterization of ice heterogeneous nucleation coupled to aerosol chemistry in WRF-Chem model version 3.5.1: evaluation through ISDAC measurements","year":2020,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Grand Équipement National De Calcul Intensif","keywords":"Weather Research and Forecasting Model; Aerosol; Ice nucleus; Nucleation; Ice crystals; Atmospheric sciences; Cloud physics; Meteorology; Climatology; Parametrization (atmospheric modeling); Chemistry; Arctic; Environmental science; Cloud computing; Physics; Geology; Radiative transfer; Oceanography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004432785,0.0002031848,0.000220742,0.00001153324,0.000133026,0.00006255623,0.0002781312,0.0001003378,0.0008230577],"category_scores_gemma":[0.0001414523,0.0002106956,0.00004629763,0.0004874387,0.00002847447,0.0002477867,0.00004439521,0.00008731012,0.00007457404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006621807,"about_ca_system_score_gemma":0.0005206599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001479825,"about_ca_topic_score_gemma":0.00006750542,"domain_scores_codex":[0.9975552,0.00002798139,0.000517484,0.0006036026,0.0009850139,0.0003107445],"domain_scores_gemma":[0.9992111,0.00002251486,0.0001753242,0.0002175132,0.0001552324,0.0002183266],"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":[0.00009085023,0.0000269601,0.0009180341,0.00005842035,0.000009367805,6.160028e-7,0.002136179,0.8577578,0.1344689,2.6244e-7,0.0003130517,0.004219585],"study_design_scores_gemma":[0.0005001914,0.00001552638,0.0003308926,0.00004602292,0.00001347741,6.158847e-7,0.00006225888,0.8278618,0.1708358,0.00004309755,0.00009499197,0.0001953116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8122429,0.00003965084,0.1865748,0.0001571216,0.0001149384,0.0004067298,0.00003502711,0.00002785758,0.0004009895],"genre_scores_gemma":[0.9339888,0.000005487095,0.06483955,0.0001885742,0.00002010935,0.000008768806,0.0006248028,0.000006874668,0.0003170133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.121746,"threshold_uncertainty_score":0.9011905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07207356344198615,"score_gpt":0.2462703789171165,"score_spread":0.1741968154751304,"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."}}