{"id":"W3033742596","doi":"10.1029/2020jd032631","title":"Improved Himawari‐8/AHI Radiance Data Assimilation With a Double Cloud Detection Scheme","year":2020,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Radiance; Cloud computing; Moderate-resolution imaging spectroradiometer; Remote sensing; Pixel; Environmental science; Cloud top; Computer science; Meteorology; Residual; Overcast; Sky; Algorithm; Artificial intelligence; Physics; Geology; Satellite","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":[0.0006093424,0.0004643912,0.0006312088,0.0003192529,0.0003290347,0.0005517461,0.0007356738,0.0003561068,0.0006916249],"category_scores_gemma":[0.000731762,0.0002388524,0.0005897186,0.0003489732,0.0001748615,0.0004586492,0.0005648276,0.0006764879,0.0002200669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004799622,"about_ca_system_score_gemma":0.0009620269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02710618,"about_ca_topic_score_gemma":0.02315856,"domain_scores_codex":[0.9996847,0.00005387665,0.00001881134,0.00008404422,0.0001128448,0.00004568873],"domain_scores_gemma":[0.9996558,0.00004642446,0.00003227917,0.00009197278,0.0001391748,0.00003424134],"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":[0.001351886,0.0007136987,0.0299822,0.0001533655,0.0003653468,0.0001621281,0.0002229644,0.5265979,0.2018025,0.003166158,0.003281596,0.2322003],"study_design_scores_gemma":[0.00004716604,0.00006700246,0.005886211,0.000002540095,0.0000237335,0.0000129385,0.000009233064,0.9802184,0.01279973,0.00008735077,0.0008229215,0.00002268021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8326178,0.0002503924,0.1604376,0.0003061985,0.0002142862,0.0001339408,0.0007353278,0.002044723,0.003259612],"genre_scores_gemma":[0.8093274,0.00004592395,0.1884059,0.00006267089,0.00003471948,0.00004478131,0.0007365773,0.00006282319,0.001279158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02710618,"threshold_uncertainty_score":0.05389684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1149582147483389,"score_gpt":0.3230466829737876,"score_spread":0.2080884682254487,"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."}}