{"id":"W2144146926","doi":"10.1109/cdc.1989.70613","title":"Model reduction of linear discrete systems via weighted impulse response Grammians","year":2003,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Eigenvalues and eigenvectors; Reduction (mathematics); Linear system; Impulse response; Realization (probability); Impulse (physics); Computer science; Mathematics; Applied mathematics; Algorithm; Control theory (sociology); Artificial intelligence; Statistics; Mathematical analysis","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":[0.000197467,0.0001319211,0.000180235,0.00005987498,0.00008783167,0.0000157011,0.00006813239,0.00004834305,0.0002205442],"category_scores_gemma":[0.000002520417,0.0001075331,0.000106264,0.0001674377,0.00004254194,0.000112564,0.00001109324,0.0001272669,0.00001737988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001524535,"about_ca_system_score_gemma":0.00004208117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001202101,"about_ca_topic_score_gemma":4.148863e-7,"domain_scores_codex":[0.999038,0.0001453349,0.0002848895,0.0002076094,0.0001306081,0.0001935431],"domain_scores_gemma":[0.9994615,0.00001968246,0.00010341,0.0002481675,0.00007245594,0.0000947392],"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":[0.001528037,0.0005071569,0.0006727161,0.00005230649,0.0002516627,0.000002138982,0.0008235187,0.5415654,0.1589157,0.2779603,0.007968555,0.00975245],"study_design_scores_gemma":[0.0004523295,0.00006528095,0.00001193958,0.00001613929,0.00002608355,0.000007885564,0.0003864476,0.9715488,0.02210769,0.00301667,0.002165027,0.0001957385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4461583,0.00003400335,0.5448499,0.0001070403,0.0004078366,0.0002240157,0.00001230433,0.00004726214,0.008159287],"genre_scores_gemma":[0.9877349,0.00000411711,0.001894434,0.000007750375,0.000145151,0.00001736442,0.00001436793,0.00001789759,0.01016398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5429555,"threshold_uncertainty_score":0.4385073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655386266047291,"score_gpt":0.2560689584948668,"score_spread":0.2395150958343939,"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."}}