{"id":"W4402209710","doi":"10.1016/j.oceaneng.2024.119138","title":"Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Canada First Research Excellence Fund; Mitacs; Dalhousie University; Ocean Frontier Institute; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Probabilistic logic; Computer science; Feature (linguistics); Automatic Identification System; Term (time); Artificial intelligence; Feature selection; Outcome (game theory); Data mining; Operations research; Machine learning; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000630217,0.0007843901,0.0006809972,0.0003988779,0.0003201655,0.0005811047,0.0009939498,0.0008029007,0.001176665],"category_scores_gemma":[0.001661294,0.0005052566,0.0007482707,0.0005629628,0.0004745165,0.001337607,0.001036001,0.001651441,0.0003481567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006116941,"about_ca_system_score_gemma":0.001065632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01314024,"about_ca_topic_score_gemma":0.0104588,"domain_scores_codex":[0.9997576,0.00004347503,0.00001468162,0.000091145,0.0000526804,0.00004044192],"domain_scores_gemma":[0.9994828,0.0002495988,0.00005950803,0.00006185838,0.0001175994,0.00002865002],"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":[0.0000495958,0.00003836917,0.0009709331,0.00002351443,0.00003301235,0.00004603769,0.00004497445,0.9309073,0.001995179,0.001573362,0.0006305279,0.06368704],"study_design_scores_gemma":[0.000001010389,0.000005912972,0.00007157034,9.985985e-7,0.000001992738,0.00000322814,0.000002120474,0.9990532,0.000292614,0.0004699156,0.0000959444,0.000001561841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04319489,0.0003119956,0.9539608,0.0002781799,0.00005002691,0.00002558053,0.0001383536,0.001088779,0.0009512666],"genre_scores_gemma":[0.8635876,0.0002021442,0.1334923,0.0001385829,0.00005395814,0.00006750545,0.0005052821,0.00007272766,0.00187981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01314024,"threshold_uncertainty_score":0.02612752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235106830417242,"score_gpt":0.2119372533056921,"score_spread":0.1995861850015196,"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."}}