{"id":"W2269111190","doi":"","title":"Behavioral learning of vessel types with fuzzy-rough decision trees","year":2014,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Larus Technologies (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Synthetic aperture radar; Dependency (UML); Fuzzy logic; Decision tree; Fuzzy set; Machine learning; Rough set; Data mining; Operations research; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002758032,0.0008165502,0.001432984,0.001637454,0.0005769806,0.001049363,0.001311131,0.0009809261,0.0006244548],"category_scores_gemma":[0.007434443,0.0005798488,0.001673209,0.0009073114,0.0006261661,0.001352355,0.0006471842,0.001492834,0.0001728288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322452,"about_ca_system_score_gemma":0.001185694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284547,"about_ca_topic_score_gemma":0.01096813,"domain_scores_codex":[0.9989145,0.0004287757,0.0001028851,0.0002351494,0.0002023937,0.0001162553],"domain_scores_gemma":[0.9947566,0.003885326,0.0004813088,0.0002038221,0.0005194906,0.0001534125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009301121,0.00008391011,0.003746937,0.0000393669,0.00005205727,0.00006008566,0.00009804904,0.9479779,0.0005233975,0.002671085,0.0003169683,0.04433722],"study_design_scores_gemma":[0.000003471059,0.00001444379,0.0002078789,0.000003792905,0.000005860611,0.000003879957,0.000007624283,0.9970402,0.0001323672,0.002539795,0.00003672203,0.000003938607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.139389,0.0002012247,0.8588805,0.00026496,0.00003057282,0.0000999177,0.0002224605,0.0002711084,0.0006401274],"genre_scores_gemma":[0.8595428,0.000124558,0.139105,0.00007595021,0.00003812168,0.0001457981,0.0005035228,0.00001704868,0.0004471984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01284547,"threshold_uncertainty_score":0.02554142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465655823223744,"score_gpt":0.2564166160944178,"score_spread":0.2417600578621804,"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."}}