{"id":"W2109593202","doi":"","title":"Track purity and current assignment ratio for target tracking and identification evaluation","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Track (disk drive); Identification (biology); Computer science; Tracking (education); Radar tracker; Sensor fusion; Tracking system; Real-time computing; Artificial intelligence; Data mining; Radar; Kalman filter; Telecommunications","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":[0.0009087325,0.0001458067,0.0001100929,0.0002105633,0.0002449083,0.0005135901,0.0003450145,0.00007536945,0.0001227931],"category_scores_gemma":[0.000163407,0.0001373501,0.00003273982,0.0000918517,0.0000436273,0.002730347,0.0001128056,0.0001352876,0.0000273659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006184313,"about_ca_system_score_gemma":0.00005841424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001084709,"about_ca_topic_score_gemma":0.000002631383,"domain_scores_codex":[0.9984459,0.00006419254,0.000496791,0.0002726499,0.0005724275,0.0001480109],"domain_scores_gemma":[0.9986101,0.00007205116,0.0003254535,0.0002362271,0.000674897,0.00008126155],"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":[0.00006174833,0.00007873676,0.0004718561,0.00002486331,0.00001222001,1.97547e-7,0.003007036,0.0001067992,0.0005493929,0.3710546,0.001265823,0.6233668],"study_design_scores_gemma":[0.001023765,0.0001303006,0.02618699,0.0001081147,0.00001435882,0.000009931219,0.0002312132,0.9226862,0.00358817,0.03510971,0.01063632,0.0002749024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02335501,0.00007606167,0.9671686,0.0008811974,0.001961444,0.0007725753,0.00006416178,0.0001287542,0.005592197],"genre_scores_gemma":[0.9890153,0.0002119324,0.01010472,0.0001618498,0.00007294177,0.0001137372,0.0002917841,0.000004502847,0.0000232623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9656603,"threshold_uncertainty_score":0.5600973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042448596321978,"score_gpt":0.3197986134460219,"score_spread":0.2155537538138241,"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."}}