{"id":"W4414297917","doi":"10.1155/atr/5582889","title":"GL‐LoiterDNet: A Hybrid Model for Ship Trajectory Prediction in Loitering Activity Scenarios","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Feature (linguistics); Nonlinear system; Artificial neural network; Control theory (sociology); Model validation; Convolutional neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002842905,0.0007315909,0.0004450561,0.000418854,0.0002226897,0.0005933956,0.001321881,0.0006852957,0.001245546],"category_scores_gemma":[0.0007873781,0.0003392315,0.000499496,0.0003578429,0.0002607077,0.0008166198,0.0007484719,0.001007675,0.0003328171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006994212,"about_ca_system_score_gemma":0.0008899982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02584799,"about_ca_topic_score_gemma":0.02830929,"domain_scores_codex":[0.9998976,0.00001114079,0.00000642305,0.00004485229,0.00001813119,0.00002188728],"domain_scores_gemma":[0.9998336,0.00005904066,0.00002301615,0.00001469115,0.00005185213,0.0000178384],"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":[0.00007490005,0.00004578967,0.003100294,0.0000285912,0.0000436619,0.00006842404,0.00002632917,0.9577872,0.00154812,0.0009163853,0.001230768,0.0351295],"study_design_scores_gemma":[0.000001440239,0.000006320377,0.0001181033,0.000001612462,0.000002813228,0.000003069011,0.000001466308,0.9994142,0.0001530462,0.0001970341,0.00009901821,0.000001897226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2246226,0.0009232456,0.7637796,0.0006046682,0.0002518707,0.00006722329,0.001514039,0.003318392,0.004918376],"genre_scores_gemma":[0.9562173,0.0002627821,0.03704487,0.0001653289,0.00003289813,0.00007787347,0.001698822,0.00008513845,0.004414821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02584799,"threshold_uncertainty_score":0.05139506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00952536637786037,"score_gpt":0.2434626320823095,"score_spread":0.2339372657044491,"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."}}