{"id":"W2068330147","doi":"10.1117/12.503715","title":"&lt;title&gt;Multisensor bias estimation using local tracks without a priori association&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Estimator; Sensor fusion; Statistics; Data association; Algorithm; Computer science; Cramér–Rao bound; Likelihood function; Association (psychology); A priori and a posteriori; Mathematics; Estimation theory; Probabilistic logic; 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":[0.0005927989,0.0004998816,0.000742158,0.0005746972,0.000311055,0.00100556,0.001082556,0.00086508,0.01997595],"category_scores_gemma":[0.001613262,0.000247585,0.0004038406,0.0008608284,0.0003859386,0.001263945,0.0007064595,0.0006190405,0.01288858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005915922,"about_ca_system_score_gemma":0.0004863226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001457778,"about_ca_topic_score_gemma":0.00248521,"domain_scores_codex":[0.9994523,0.00008095586,0.00003189838,0.0001456913,0.0002576103,0.00003150881],"domain_scores_gemma":[0.9991185,0.0001848699,0.00008841506,0.0003059953,0.0002738405,0.0000283724],"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":[0.0003231352,0.00005539099,0.001337547,0.0002798351,0.0000608697,0.0002333073,0.00005702309,0.06196757,0.04582085,0.02310677,0.03153975,0.8352179],"study_design_scores_gemma":[0.00006274655,0.0002881817,0.002485924,0.0001035394,0.00005642466,0.0006227259,0.00004286012,0.7474056,0.09232833,0.01702519,0.1394745,0.0001039777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004439789,0.0008165364,0.9845597,0.0002877303,0.000392428,0.0000525439,0.0002044868,0.001664103,0.007582669],"genre_scores_gemma":[0.2383912,0.002306664,0.6445052,0.0004823575,0.001045359,0.0002261798,0.001957221,0.001407239,0.1096786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01997595,"threshold_uncertainty_score":0.06682622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804811974538723,"score_gpt":0.2417796517794922,"score_spread":0.223731532034105,"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."}}