{"id":"W3091914433","doi":"10.1109/jiot.2020.3028743","title":"Data-Driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-Enabled Maritime IoT Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":186,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"State Key Laboratory of Industrial Control Technology; Deanship of Scientific Research, King Saud University; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Computer science; Trajectory; Real-time computing; Data acquisition; Outlier; Wireless; Artificial intelligence; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006275465,0.0004783834,0.0003831644,0.0004054275,0.0003969462,0.0004770733,0.0007939299,0.0004401733,0.0008340906],"category_scores_gemma":[0.00186164,0.0001679451,0.0003919298,0.0005941491,0.000302612,0.00112327,0.0006564754,0.0007096275,0.0002437157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006439378,"about_ca_system_score_gemma":0.0008193849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007427261,"about_ca_topic_score_gemma":0.006085021,"domain_scores_codex":[0.9996794,0.00004844757,0.0000203252,0.00007027414,0.0001382911,0.00004321704],"domain_scores_gemma":[0.9994298,0.0001218825,0.00007892556,0.00007489466,0.0002644862,0.00003006693],"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":[0.0002719551,0.0001519292,0.00591773,0.0001248621,0.00004553024,0.0001775654,0.0001555493,0.7662994,0.02631081,0.003979081,0.001682075,0.1948834],"study_design_scores_gemma":[0.000004651835,0.00002631032,0.0005144862,0.000002545555,0.000005834029,0.0000164891,0.00001038219,0.9955914,0.003079049,0.0004465875,0.0002984227,0.000003834784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.128082,0.0003144497,0.8679632,0.0002736007,0.00005555133,0.00006053669,0.0001302871,0.001171533,0.001948834],"genre_scores_gemma":[0.9399464,0.0001396973,0.05878134,0.00005023488,0.00001749781,0.00003562823,0.0002939659,0.00004049067,0.0006947813],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007427261,"threshold_uncertainty_score":0.01476806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03494355838399087,"score_gpt":0.2781458382369897,"score_spread":0.2432022798529989,"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."}}