{"id":"W2767638814","doi":"10.3390/rs9111142","title":"Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Goddard Space Flight Center; National Natural Science Foundation of China; Japan Aerospace Exploration Agency; Canada Excellence Research Chairs, Government of Canada; National Aeronautics and Space Administration","keywords":"Global Precipitation Measurement; Precipitation; Environmental science; Radar; Meteorology; Precipitation types; Climatology; Remote sensing; Geology; Computer science; Geography","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":[0.001151737,0.0002141978,0.000299474,0.0001857937,0.0004087459,0.0002602609,0.0001022155,0.0001348823,0.00001413327],"category_scores_gemma":[0.001342282,0.0002000475,0.00004282509,0.0002417853,0.0001491806,0.001149329,0.00001988485,0.0001334438,0.000006552325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004782225,"about_ca_system_score_gemma":0.0000973943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001178388,"about_ca_topic_score_gemma":0.002418454,"domain_scores_codex":[0.9979379,0.0002923202,0.0006245995,0.0004436538,0.0004294329,0.0002721487],"domain_scores_gemma":[0.998682,0.000212256,0.0003965363,0.0002841749,0.0003218106,0.0001032341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004987976,0.00005172049,0.2954634,0.0004202987,0.0001423991,0.000008279195,0.003218486,0.002788902,0.06787948,0.0005598447,0.00002528023,0.6289431],"study_design_scores_gemma":[0.0007074418,0.0001071309,0.5587528,0.0001523413,0.00007234467,0.000003122671,0.0004458072,0.4301524,0.001857037,0.007572585,0.000005192393,0.0001718256],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918191,0.0002884677,0.005833529,0.0006491874,0.0002713121,0.0004657735,0.00002223175,0.00003458014,0.0006157636],"genre_scores_gemma":[0.962526,0.0002095852,0.0368798,0.00003302999,0.00005811009,1.297852e-7,0.0002619064,0.000007700295,0.00002373336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6287713,"threshold_uncertainty_score":0.8157701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03401608123823094,"score_gpt":0.2740208816536024,"score_spread":0.2400048004153714,"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."}}