{"id":"W2791318716","doi":"10.1109/camsap.2017.8313189","title":"Gaussian sum particle flow filter","year":2017,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Gaussian; Particle filter; Algorithm; Computer science; Gaussian filter; Applied mathematics; Dimension (graph theory); Flow (mathematics); Ensemble Kalman filter; Nonlinear system; Importance sampling; Mathematical optimization; Likelihood function; Mathematics; Kalman filter; Estimation theory; Artificial intelligence; Extended Kalman filter; Statistics; Monte Carlo method; Physics","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":[0.0001553279,0.00008271978,0.00008536561,0.00001393695,0.0005287975,0.0007843532,0.001380609,0.00006532783,0.0003274943],"category_scores_gemma":[0.00004517597,0.00006373601,0.00004198949,0.00003747404,0.00005096194,0.0006829653,0.000435209,0.0001337633,0.0006612248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005812215,"about_ca_system_score_gemma":0.00001155841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004861012,"about_ca_topic_score_gemma":0.00002665043,"domain_scores_codex":[0.9991471,0.00002088595,0.0001209971,0.0002796857,0.000158897,0.0002724965],"domain_scores_gemma":[0.9980878,0.00003483765,0.00005456289,0.001679633,0.0000271231,0.0001160529],"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":[0.00001121707,0.0001490797,0.022794,0.000009050752,0.00002403817,0.0001319628,0.0004832059,0.0004895275,0.0009011307,0.2576665,0.3168091,0.4005312],"study_design_scores_gemma":[0.0005341991,0.0000500022,0.0606938,0.00002266336,0.000004550318,0.00002534064,0.00001389581,0.757627,0.007070377,0.004771302,0.1688228,0.0003641162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01935349,0.00005055276,0.8911459,0.01090016,0.002077092,0.00009634948,0.000006009795,0.0005472932,0.07582318],"genre_scores_gemma":[0.9124333,0.000008733811,0.08282568,0.0007234139,0.0001621647,0.000003429442,0.00000205093,0.000005553256,0.003835628],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8930799,"threshold_uncertainty_score":0.8498928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961222850576792,"score_gpt":0.2647144685137243,"score_spread":0.2351022400079564,"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."}}