{"id":"W3088327449","doi":"10.1109/access.2020.3025302","title":"Flash Flood Detection From CYGNSS Data Using the RUSBoost Algorithm","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Flash flood; Support vector machine; Computer science; Feature selection; Flood myth; Data mining; Observable; Artificial intelligence; Ancillary data; Algorithm; Pattern recognition (psychology); 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":[0.00136714,0.001163147,0.001595413,0.002107349,0.0005351971,0.000759933,0.001459865,0.001410022,0.001821654],"category_scores_gemma":[0.001682428,0.0004691269,0.0009912183,0.001290839,0.0003676272,0.0005993939,0.0005174725,0.0008377471,0.0007540043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000645857,"about_ca_system_score_gemma":0.001532839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055779,"about_ca_topic_score_gemma":0.007060365,"domain_scores_codex":[0.9993564,0.000105322,0.00005671761,0.0001741805,0.0001489987,0.0001583509],"domain_scores_gemma":[0.999421,0.0002125044,0.00007090794,0.00002867381,0.0002344511,0.00003251937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008843339,0.0006184479,0.007018735,0.0002260685,0.0002277023,0.0001877203,0.00009763478,0.4199415,0.0120255,0.0008741402,0.006650866,0.5512475],"study_design_scores_gemma":[0.00001860161,0.00006034013,0.001078364,0.000008642067,0.0000112167,0.00001957247,0.00001929446,0.9967088,0.001384424,0.0002730959,0.000410508,0.000007146442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3127572,0.001359432,0.6741467,0.0003633721,0.000254597,0.0004840136,0.0006181124,0.006408492,0.003608117],"genre_scores_gemma":[0.7533152,0.0002905553,0.2391776,0.0001841892,0.0001011617,0.0004667758,0.001961399,0.0001484626,0.004354559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01055779,"threshold_uncertainty_score":0.0209927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1045946021029169,"score_gpt":0.3236586571952009,"score_spread":0.2190640550922839,"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."}}