{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002607205,0.0001653364,0.0001173014,0.0002527384,0.0001565956,0.0001472282,0.0005677583,0.00009212345,0.0004175068],"category_scores_gemma":[0.0001163322,0.000146427,0.00007097475,0.0001280283,0.0000311814,0.001976968,0.0001562616,0.0001836698,0.0004557437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004312214,"about_ca_system_score_gemma":0.0000661152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002567666,"about_ca_topic_score_gemma":0.00000218158,"domain_scores_codex":[0.9985679,0.00002615576,0.0004899162,0.0002250209,0.0004820939,0.0002089515],"domain_scores_gemma":[0.9985629,0.00009728515,0.0002632433,0.0003316601,0.0006702821,0.00007466292],"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":[0.0004339334,0.0002343598,0.0001526105,0.00002893069,0.00004593108,0.000008468394,0.003317867,0.004644212,0.0005692964,0.6845846,0.03514873,0.270831],"study_design_scores_gemma":[0.0006694663,0.0001387886,0.0006336794,0.00005441085,0.000002625807,0.00001151078,0.0000279797,0.9414942,0.001845953,0.006160757,0.04877147,0.0001891459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005497258,0.000002043163,0.9804537,0.004444029,0.001293644,0.0003028504,0.0001219306,0.000228124,0.007656456],"genre_scores_gemma":[0.3016177,0.00002155477,0.6858544,0.01077979,0.0001411657,0.00008545499,0.001094843,0.00001194476,0.0003931753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.93685,"threshold_uncertainty_score":0.5971121,"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."}}