{"id":"W3167855819","doi":"10.3390/ijgi10060412","title":"A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Ocean Frontier Institute","keywords":"Computer science; Visual analytics; Anomaly detection; TRIPS architecture; Domain (mathematical analysis); Analytics; Visualization; Bridge (graph theory); Interpolation (computer graphics); Trajectory; Task (project management); Data mining; Anomaly (physics); Artificial intelligence; Data science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004257004,0.0001054531,0.000153094,0.000559898,0.00008798614,0.0002895218,0.0003894396,0.00008463531,0.00001237735],"category_scores_gemma":[0.00009440133,0.0001140953,0.0001598279,0.0003879208,0.00002393581,0.003378831,0.00006671374,0.0001892581,0.000005827661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004084569,"about_ca_system_score_gemma":0.0002377819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002148274,"about_ca_topic_score_gemma":0.00002459544,"domain_scores_codex":[0.9985447,0.00003011025,0.0007658612,0.0001014811,0.0004053779,0.0001524567],"domain_scores_gemma":[0.9983056,0.00006834758,0.0004752206,0.0001175286,0.0009838763,0.00004945809],"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":[0.0001345612,0.0001700392,0.0004136722,0.00003732567,0.0001093838,0.00002340003,0.0006508285,0.1048313,0.004131543,0.004509578,0.0001132383,0.8848751],"study_design_scores_gemma":[0.0008637289,0.0001386745,0.003223368,0.00007538459,0.00001703127,0.0005036413,0.0002313684,0.9673709,0.02257156,0.000731973,0.004101254,0.0001710734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.190472,0.0000190762,0.8086091,0.0002033087,0.0004899634,0.0001069741,0.000006336253,0.00003238471,0.00006087394],"genre_scores_gemma":[0.9514818,0.00002198523,0.04808255,0.0002297281,0.0001451985,0.00001104937,0.000008774376,0.000004774274,0.00001415756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8847041,"threshold_uncertainty_score":0.4652673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117916609232504,"score_gpt":0.2819742111157838,"score_spread":0.2701825501925334,"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."}}