{"id":"W2543883930","doi":"10.1109/iecon.2002.1182812","title":"Consistent fusion of correlated data sources","year":2003,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Covariance intersection; Kalman filter; Sensor fusion; Fusion; Intersection (aeronautics); Covariance; Computer science; Ellipsoid; Filter (signal processing); Extended Kalman filter; Tracking (education); Algorithm; Fast Kalman filter; Artificial intelligence; Mathematics; Computer vision; Engineering; Statistics","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":[0.0003260601,0.00007656517,0.0001199336,0.00004441525,0.00007078217,0.00004538482,0.001007747,0.00005334057,0.0001992882],"category_scores_gemma":[0.000100398,0.00005953437,0.00002424594,0.0002479452,0.00004527399,0.0002252915,0.000422472,0.00008596926,0.0000528807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003941611,"about_ca_system_score_gemma":0.00003105784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002801286,"about_ca_topic_score_gemma":0.000005646713,"domain_scores_codex":[0.9990278,0.00007550629,0.0002306347,0.0003157219,0.0001997624,0.0001505088],"domain_scores_gemma":[0.9982294,0.0001436995,0.00008288087,0.001426448,0.00005785337,0.00005972751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000207235,0.0005428844,0.01980563,0.00004181987,0.00008385371,0.00005115044,0.0007761922,0.002718619,0.00365089,0.7072873,0.1899732,0.07504784],"study_design_scores_gemma":[0.0015647,0.0001985334,0.004915291,0.0001368248,0.00003960696,0.0001956195,0.0003123115,0.4208204,0.01404919,0.004309466,0.5527112,0.0007467942],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04935054,0.0006642269,0.9103022,0.0002667947,0.001396795,0.0001298488,0.0000214709,0.0003185909,0.03754958],"genre_scores_gemma":[0.922513,0.00006119107,0.07644109,0.0001961724,0.00001449536,5.892028e-7,0.00003395653,0.000004906323,0.0007346089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8731624,"threshold_uncertainty_score":0.2427741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399631900814852,"score_gpt":0.2522066245945679,"score_spread":0.2082103055864194,"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."}}