{"id":"W2080093103","doi":"10.1109/cisda.2014.7035641","title":"Risk management with hard-soft data fusion in maritime domain awareness","year":2014,"lang":"en","type":"article","venue":"","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Larus Technologies (Canada)","funders":"","keywords":"Situation awareness; Computer science; Sensor fusion; Risk management; Domain (mathematical analysis); Process (computing); Metric (unit); Risk analysis (engineering); Data mining; Soft computing; Data integration; Situation analysis; Data science; Machine learning; Engineering; Operations management; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003636547,0.0001054722,0.0001159863,0.00006631478,0.00003944071,0.00003006775,0.0002488238,0.00004315863,0.0006420512],"category_scores_gemma":[0.000006802975,0.00008941332,0.00001081934,0.0001557675,0.0000136429,0.0001337411,0.0001270956,0.0001102659,0.0001130816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002680038,"about_ca_system_score_gemma":0.000003962947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167005,"about_ca_topic_score_gemma":0.0005428616,"domain_scores_codex":[0.9992858,0.00003638335,0.0001559988,0.000206106,0.0001497617,0.0001659943],"domain_scores_gemma":[0.9993382,0.00003486591,0.00001403677,0.000550627,0.00001084778,0.00005143417],"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":[0.0001575237,0.0002483561,0.2161158,0.0009959852,0.0001998555,0.00009921104,0.0003320112,0.05791146,0.0001908686,0.05093281,0.04082712,0.631989],"study_design_scores_gemma":[0.001924632,0.00002802316,0.1371337,0.00012312,0.00002310021,0.000005540648,0.0001016887,0.5939292,0.00008072038,0.00151341,0.2647212,0.0004156695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06318169,0.00005718866,0.5832875,0.0002304577,0.0002074716,0.0004274368,0.00008163947,0.0006635702,0.351863],"genre_scores_gemma":[0.9458287,0.0001900751,0.05060353,0.0001958977,0.00006785622,0.00002522528,0.0005392357,0.0000458928,0.002503547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.882647,"threshold_uncertainty_score":0.703001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026185026394951,"score_gpt":0.2165152389363639,"score_spread":0.2062533886724144,"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."}}