{"id":"W2083468814","doi":"10.1109/acssc.2001.987696","title":"IMM-JVC and IMM-JPDA for closely maneuvering targets","year":2001,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada); McGill University","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.002282476,0.0005385969,0.000807069,0.0009837811,0.0006762629,0.001418408,0.001305445,0.001175457,0.001443869],"category_scores_gemma":[0.01012214,0.0003490051,0.0004385762,0.001181553,0.0008007533,0.001470744,0.001572273,0.001406036,0.0008491231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005375996,"about_ca_system_score_gemma":0.002214717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002845665,"about_ca_topic_score_gemma":0.004339577,"domain_scores_codex":[0.9986307,0.0003602481,0.00006846137,0.0002107357,0.0006180797,0.00011173],"domain_scores_gemma":[0.9958864,0.001357635,0.0004298404,0.0009933371,0.001186918,0.000145838],"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":[0.0003234321,0.0001178748,0.0036882,0.0001988082,0.0000746383,0.0001101114,0.0001808011,0.2786632,0.01239501,0.03292815,0.003442452,0.6678774],"study_design_scores_gemma":[0.00002241466,0.00008865585,0.0008592951,0.00001485386,0.00001558169,0.000160223,0.00003592903,0.9810736,0.006953625,0.004677122,0.00607107,0.00002769145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006456043,0.0002107865,0.9911897,0.00009566747,0.00007279046,0.00003624319,0.00002882502,0.0004698228,0.001440127],"genre_scores_gemma":[0.2352537,0.0001994278,0.7618392,0.00008357818,0.00005456449,0.0001316907,0.0001620989,0.0000713754,0.002204327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002845665,"threshold_uncertainty_score":0.01207101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562096641992762,"score_gpt":0.2389250724408098,"score_spread":0.2233041060208822,"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."}}