{"id":"W2294780743","doi":"10.1049/iet-spr.2015.0360","title":"Unbiased, optimal, and in‐betweens: the trade‐off in discrete finite impulse response filtering","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Royal Academy of Engineering","keywords":"Finite impulse response; Kalman filter; Mathematics; Robustness (evolution); Mean squared error; Gaussian; Algorithm; Control theory (sociology); Applied mathematics; Statistics; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005767026,0.001102953,0.001697803,0.001109728,0.0005407824,0.002928756,0.001309576,0.003145021,0.001709801],"category_scores_gemma":[0.01921873,0.0009983558,0.0009399864,0.001331893,0.001809441,0.004391972,0.001149313,0.001634343,0.0006973729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003841,"about_ca_system_score_gemma":0.0009050377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001626806,"about_ca_topic_score_gemma":0.001332773,"domain_scores_codex":[0.9966858,0.001182619,0.0002983009,0.0006042757,0.000995717,0.0002332827],"domain_scores_gemma":[0.9885769,0.009504703,0.0003937621,0.0007873669,0.0006385505,0.00009876626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002452335,0.00009063644,0.001590146,0.0006770276,0.0001835944,0.00011514,0.0002879607,0.2313706,0.00573794,0.3210614,0.002707156,0.4359332],"study_design_scores_gemma":[0.00002337846,0.0002166661,0.0009008147,0.00019031,0.00009082623,0.0002694706,0.00010103,0.7910108,0.00598247,0.1935552,0.007568318,0.00009072026],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005676517,0.00548329,0.9851345,0.0002845418,0.00006874499,0.00001140504,0.00002030353,0.0001431824,0.003177346],"genre_scores_gemma":[0.4944277,0.01855275,0.4778853,0.0006171589,0.0007490487,0.000116357,0.0002197562,0.0004680416,0.006963854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005767026,"threshold_uncertainty_score":0.03049934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933617042685448,"score_gpt":0.2571215455330577,"score_spread":0.2377853751062032,"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."}}