{"id":"W2038391943","doi":"10.1117/12.734552","title":"<title>Spline filter for nonlinear/non-Gaussian Bayesian tracking</title>","year":2007,"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":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Probability density function; Algorithm; Spline (mechanical); Nonlinear system; Computer science; Gaussian; Particle filter; Spline interpolation; Applied mathematics; Mathematics; Probability distribution; Mathematical optimization; Artificial intelligence; Kalman filter; Statistics; Computer vision","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":[0.0007819739,0.0004553419,0.0005768299,0.0006725095,0.0003732702,0.0006338258,0.0008670296,0.0009017582,0.008739116],"category_scores_gemma":[0.001852348,0.0002354227,0.0005799721,0.000951706,0.0004441311,0.0006224589,0.0004280513,0.001078464,0.003582431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005163823,"about_ca_system_score_gemma":0.0007467114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077185,"about_ca_topic_score_gemma":0.00542986,"domain_scores_codex":[0.9995776,0.00009712047,0.00001885656,0.00006519788,0.0002088295,0.00003233728],"domain_scores_gemma":[0.9995013,0.0001530699,0.00004438611,0.0001014894,0.0001660367,0.00003353971],"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":[0.0003313548,0.00008091296,0.0007719903,0.0001929194,0.00006028374,0.0002060858,0.0001049596,0.1314183,0.03411241,0.09052382,0.01147129,0.7307257],"study_design_scores_gemma":[0.0000156617,0.00004581171,0.0003337244,0.00001803208,0.000008904972,0.00009384679,0.000008381945,0.9614593,0.009742812,0.01087696,0.01736928,0.00002723043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001054793,0.00009584137,0.9971898,0.00004711289,0.00006794331,0.00001382581,0.00003501627,0.0003340882,0.001161577],"genre_scores_gemma":[0.09318283,0.0005715899,0.8903795,0.0001064726,0.0001490908,0.0001008171,0.0005031137,0.0002897462,0.01471685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008739116,"threshold_uncertainty_score":0.02923524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178234486940407,"score_gpt":0.2384593270720866,"score_spread":0.2266769822026825,"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."}}