{"id":"W2068330147","doi":"10.1117/12.503715","title":"<title>Multisensor bias estimation using local tracks without a priori association</title>","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005360565,0.0001902839,0.0002372947,0.00007456605,0.00008349939,0.0001491759,0.0005946984,0.0001820578,0.0000477733],"category_scores_gemma":[0.000763217,0.000166549,0.0002669702,0.0002874911,0.00007469795,0.0003883116,0.0000827765,0.0002270531,0.00002225626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001932796,"about_ca_system_score_gemma":0.00004954999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002092859,"about_ca_topic_score_gemma":6.418698e-8,"domain_scores_codex":[0.9984159,5.335699e-8,0.0004136642,0.0002941712,0.0005935718,0.0002826513],"domain_scores_gemma":[0.998672,0.0001287361,0.0002969228,0.00007166982,0.000732842,0.00009781907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001106418,0.00008314041,0.0002599582,0.0001190594,0.0001683927,1.919794e-7,0.0001328477,0.001232433,0.04331086,0.9234654,0.02791711,0.003299538],"study_design_scores_gemma":[0.0007741376,0.0001025342,0.0002877708,0.0002690175,0.0001069401,0.00003233115,0.0001110344,0.8893997,0.02116934,0.003411363,0.08386334,0.0004725176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9399859,0.0001895955,0.03018783,0.0009645318,0.001245932,0.0004405404,0.00003969018,0.0002510534,0.02669498],"genre_scores_gemma":[0.3008847,0.0000714206,0.6969039,0.0001373253,0.0004076654,0.0000289868,0.000008937804,0.00005544192,0.001501563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.920054,"threshold_uncertainty_score":0.679167,"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."}}