{"id":"W3094962305","doi":"10.1109/mfi49285.2020.9235243","title":"Detecting Floods Caused by Tropical Cyclone Using CYGNSS Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Flash flood; Tropical cyclone; Cyclone (programming language); Flood myth; Environmental science; Meteorology; Remote sensing; Precipitation; Computer science; Geology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002467769,0.0005063998,0.0002636558,0.001626673,0.0002121858,0.0003223738,0.0002098727,0.0002863265,0.0005178516],"category_scores_gemma":[0.000527305,0.00009861704,0.0002110613,0.00082876,0.0001485753,0.0002692059,0.0003148774,0.0001913797,0.0001691127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002750157,"about_ca_system_score_gemma":0.000394776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02424087,"about_ca_topic_score_gemma":0.03054696,"domain_scores_codex":[0.9998801,0.00001895124,0.000008997511,0.00003049932,0.00003805986,0.00002350646],"domain_scores_gemma":[0.9998411,0.00003461175,0.00003154395,0.00001762432,0.00005663196,0.00001847147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008229735,0.0002390719,0.5503323,0.0002383318,0.0002512498,0.001588554,0.0003824134,0.09534209,0.1033684,0.0005161102,0.006200846,0.2407177],"study_design_scores_gemma":[0.00004126916,0.0001442154,0.5801245,0.00003340275,0.00008392551,0.000229951,0.0003771192,0.3929332,0.02317699,0.0002391465,0.002573658,0.00004267944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931887,0.0001091183,0.003873729,0.00004229655,0.00002423599,0.00002830992,0.001036305,0.000376098,0.001321037],"genre_scores_gemma":[0.9934109,0.00008456413,0.004044,0.000009485915,0.00001214598,0.00001365151,0.001922507,0.000008656798,0.0004940004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02424087,"threshold_uncertainty_score":0.04819953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05809032521813554,"score_gpt":0.286544657705221,"score_spread":0.2284543324870854,"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."}}