{"id":"W3170605318","doi":"10.3390/rs13112164","title":"Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Clutter; Computer science; Radar; Trajectory; Convolutional neural network; Artificial intelligence; Autoregressive model; Remote sensing; Computer vision; Algorithm; Geology; Telecommunications; Mathematics","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":[0.0002997461,0.0007441155,0.0005716586,0.0004368523,0.0002956897,0.0004897831,0.0007776028,0.000559094,0.0008236734],"category_scores_gemma":[0.0007414108,0.0003687513,0.0006557998,0.0004543248,0.000221642,0.0005599241,0.0004713833,0.0008540309,0.0002537731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005612754,"about_ca_system_score_gemma":0.0007252541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02842649,"about_ca_topic_score_gemma":0.02163414,"domain_scores_codex":[0.9998496,0.000009374052,0.000008765722,0.00005508596,0.00003670769,0.00004036999],"domain_scores_gemma":[0.9997907,0.00004261003,0.00003561526,0.00001856445,0.00009020002,0.00002221959],"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":[0.0001366453,0.00008382551,0.007717438,0.00004637799,0.00008587952,0.0001784385,0.0000581682,0.8776667,0.008050596,0.001142982,0.001740039,0.103093],"study_design_scores_gemma":[0.000001308399,0.000005849281,0.0004771967,0.000001111305,0.000004835957,0.000004012228,0.0000016028,0.9990381,0.0003121072,0.000101574,0.00005002242,0.000002213292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4048112,0.001035376,0.5871119,0.0004485669,0.0002814058,0.00003776008,0.0003837442,0.00165435,0.004235665],"genre_scores_gemma":[0.9791294,0.0001845091,0.01766921,0.00007222607,0.00004382927,0.00002298647,0.000420229,0.00002808748,0.002429471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02842649,"threshold_uncertainty_score":0.05652207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169053075776707,"score_gpt":0.2117562645602001,"score_spread":0.2000657338024331,"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."}}