{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001503032,0.001562011,0.000670967,0.007116939,0.0004370629,0.002273164,0.001246311,0.0007259598,0.01171939],"category_scores_gemma":[0.008603082,0.0003423891,0.0007080751,0.002932357,0.0003109474,0.002377087,0.001912753,0.000998634,0.003331505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004643627,"about_ca_system_score_gemma":0.0006869226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00476689,"about_ca_topic_score_gemma":0.005133819,"domain_scores_codex":[0.9991705,0.0001526408,0.000094216,0.000152674,0.0003644021,0.00006565178],"domain_scores_gemma":[0.9957252,0.001987589,0.0004707545,0.000440953,0.001060362,0.0003152263],"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":[0.001030242,0.0007462489,0.02164289,0.00104814,0.0001767785,0.0007429065,0.002894981,0.01933123,0.03020366,0.007664797,0.06692234,0.8475959],"study_design_scores_gemma":[0.0002186449,0.0006170354,0.03545595,0.0005475921,0.0001302213,0.0009339852,0.002644351,0.8029557,0.05462888,0.02474579,0.07674635,0.0003755115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.061209,0.0002409825,0.7379443,0.0004834065,0.0001437817,0.000733988,0.01431219,0.1774545,0.007477844],"genre_scores_gemma":[0.3242435,0.0002627069,0.6536275,0.0001487491,0.00005969289,0.0007632231,0.01342637,0.003644134,0.003824111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01171939,"threshold_uncertainty_score":0.03920519,"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."}}