{"id":"W4386280779","doi":"10.1109/jsac.2023.3310109","title":"Digital Twins Based Intelligent State Prediction Method for Maneuvering-Target Tracking","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Exfo Electro-Optical Engineering (Canada)","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Liuzhou Science and Technology Project; Guangdong Science and Technology Department; Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Tracking (education); Artificial intelligence; Generalization; Artificial neural network; Noise (video); Motion (physics); Computer vision; Tracking system; State (computer science); Pattern recognition (psychology); Kalman filter; Algorithm; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006753667,0.000147373,0.0002549315,0.0005449813,0.0002433357,0.0001399465,0.0003254947,0.0000702644,0.00001315478],"category_scores_gemma":[0.0007326045,0.0001349942,0.000117163,0.0009178725,0.0000572457,0.0001688811,0.00003209326,0.0008755982,0.00001337267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003035677,"about_ca_system_score_gemma":0.0001785231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006190097,"about_ca_topic_score_gemma":0.000009644963,"domain_scores_codex":[0.998654,0.0001370431,0.0004812221,0.0001724706,0.0002366207,0.0003186266],"domain_scores_gemma":[0.9979035,0.0008421921,0.0001251113,0.0006076526,0.0003695114,0.0001520604],"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":[0.00421283,0.01157318,0.156403,0.0006049125,0.001387755,0.0003692803,0.005064664,0.03455062,0.1634624,0.004132708,0.06408968,0.554149],"study_design_scores_gemma":[0.002126829,0.001742433,0.02613863,0.001195537,0.0001284814,0.0003129608,0.0002076048,0.8607547,0.06077559,0.006856753,0.0393498,0.0004107173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05168506,0.0001243359,0.9304974,0.01099453,0.0003104999,0.0009887957,0.0001551557,0.001038348,0.004205836],"genre_scores_gemma":[0.8467462,0.000444636,0.1514999,0.0004074808,0.0001064437,0.000103476,0.0001762739,0.0000536547,0.0004619259],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8262041,"threshold_uncertainty_score":0.5504903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0591814001899333,"score_gpt":0.392995209758408,"score_spread":0.3338138095684747,"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."}}