{"id":"W4251578657","doi":"10.5194/amt-2019-369","title":"Synergistic radar and radiometer retrievals of ice hydrometeors","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universität Hamburg; Swedish National Space Agency; Environment and Climate Change Canada; Deutsche Forschungsgemeinschaft","keywords":"Remote sensing; Radiometer; Satellite; Environmental science; Ice cloud; Microwave radiometer; Radar; Meteorology; Microwave; Atmosphere (unit); Computer science; Geology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007540117,0.0006075412,0.0007888656,0.0006582632,0.000130908,0.0006246694,0.0005123389,0.0004485432,0.0007418123],"category_scores_gemma":[0.0009668643,0.0004300745,0.0007792795,0.000697553,0.0001716251,0.001133096,0.0006424063,0.0003635754,0.000292974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002892483,"about_ca_system_score_gemma":0.0003181803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821974,"about_ca_topic_score_gemma":0.002469439,"domain_scores_codex":[0.9996487,0.00008784056,0.00001645741,0.00008402453,0.0001138258,0.0000491559],"domain_scores_gemma":[0.9997157,0.00009238709,0.00004187601,0.00004714321,0.00008012733,0.00002275513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00122666,0.0003863742,0.03316825,0.0004375247,0.0006236522,0.0002438131,0.00016291,0.2568926,0.5439984,0.001473148,0.001155367,0.1602315],"study_design_scores_gemma":[0.0002319799,0.0003072618,0.02270772,0.00002120447,0.0002400932,0.00009247416,0.00005267894,0.8859769,0.08879571,0.0003754899,0.001151164,0.00004737251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.950102,0.0006545457,0.04453773,0.00009372845,0.00008087922,0.00006211803,0.0004574198,0.0005235901,0.003488038],"genre_scores_gemma":[0.9678597,0.0001348912,0.03082014,0.0000321996,0.00003918719,0.00002076046,0.0004992328,0.00004009813,0.0005536812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001821974,"threshold_uncertainty_score":0.00398767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009256173589083396,"score_gpt":0.2193608285085471,"score_spread":0.2101046549194637,"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."}}