{"id":"W2964360779","doi":"10.3390/atmos10080433","title":"Simulating Arctic Ice Clouds during Spring Using an Advanced Ice Cloud Microphysics in the WRF Model","year":2019,"lang":"en","type":"article","venue":"Atmosphere","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Weather Research and Forecasting Model; Ice crystals; Ice nucleus; Environmental science; Arctic; Atmospheric sciences; Meteorology; Spring (device); Climatology; Lidar; Nucleation; Geology; Remote sensing; Geography; Oceanography; Physics","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":[0.000242405,0.0002834929,0.0002486574,3.078732e-7,0.0002686513,0.00007314279,0.0005718368,0.000101404,0.0003914227],"category_scores_gemma":[0.00001773076,0.0002360988,0.00009020368,0.0003979529,0.00009253508,0.0005237603,0.0002844301,0.0003665866,0.000213353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003016921,"about_ca_system_score_gemma":0.00002086348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962848,"about_ca_topic_score_gemma":0.0002793639,"domain_scores_codex":[0.9980307,0.00009162306,0.0003310402,0.000560431,0.0003948915,0.0005912859],"domain_scores_gemma":[0.9989873,0.00007301225,0.0001600312,0.0006765361,0.00001157201,0.00009152188],"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":[0.00002403902,0.00008000148,0.1235668,0.00002414951,0.000005379993,0.000009533333,0.001453431,0.8342887,0.03914168,0.00008780331,0.0000019659,0.001316524],"study_design_scores_gemma":[0.0007638904,0.00006501655,0.05491328,0.00007861696,0.00002172322,0.000009744126,0.002522985,0.93975,0.0008246571,0.0004832608,0.0001154556,0.0004513565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949694,0.0000776461,0.001763151,0.00003748885,0.0001590061,0.0003782405,0.000001059153,0.0000515078,0.002562522],"genre_scores_gemma":[0.9793143,0.000009877724,0.01982663,0.0003274939,0.0001097051,0.000008949693,0.000001185641,0.00005454285,0.0003473655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1054614,"threshold_uncertainty_score":0.9627828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143508709867382,"score_gpt":0.2342787429441081,"score_spread":0.2228436558454343,"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."}}