{"id":"W2122374289","doi":"","title":"A spline filter for multidimensional nonlinear state estimation","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":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Particle filter; Markov chain Monte Carlo; Monte Carlo method; Spline (mechanical); Algorithm; Gaussian; Nonlinear system; Computer science; Mathematical optimization; Mathematics; State space; Filter (signal processing); Auxiliary particle filter; Hybrid Monte Carlo; Applied mathematics; Kalman filter; Ensemble Kalman filter; Extended Kalman filter; Artificial intelligence; Statistics; Engineering","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":[0.001326366,0.0004698721,0.0007600438,0.0006242368,0.0004354215,0.000605282,0.0007131531,0.001170605,0.001690934],"category_scores_gemma":[0.003073509,0.000319206,0.0009397997,0.001067439,0.0004773988,0.0007640422,0.0007938766,0.001424422,0.0006119827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004945985,"about_ca_system_score_gemma":0.001383897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005624513,"about_ca_topic_score_gemma":0.005040639,"domain_scores_codex":[0.9995129,0.0001390925,0.00002690388,0.00008577337,0.0001964252,0.00003885569],"domain_scores_gemma":[0.9993141,0.0003573444,0.00005428685,0.00006869592,0.0001726339,0.00003291772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001703359,0.0000621,0.0008016431,0.0001686597,0.00007979692,0.0001104939,0.0001168387,0.6499075,0.01236263,0.04821497,0.001637862,0.2863671],"study_design_scores_gemma":[0.00000428882,0.00001907552,0.00008677741,0.00000615966,0.000004604629,0.00002110534,0.000003438233,0.9949148,0.0008467253,0.002999177,0.001085098,0.000008745272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001372007,0.00009878852,0.9981281,0.0000287264,0.00002367045,0.000006067369,0.00001173093,0.00007952216,0.0002513358],"genre_scores_gemma":[0.2189389,0.0009812476,0.7731549,0.0000920204,0.0001167835,0.0001522019,0.0002840604,0.00008355173,0.006196234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005624513,"threshold_uncertainty_score":0.0111835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05232170939670668,"score_gpt":0.2810778122672766,"score_spread":0.2287561028705699,"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."}}