{"id":"W2000122651","doi":"10.1006/mssp.2000.1289","title":"MODEL ORDER SELECTION: A PRACTICAL APPROACH","year":2001,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Autoregressive model; Bayesian information criterion; Minimum description length; Information Criteria; Model selection; Selection (genetic algorithm); Range (aeronautics); Process (computing); Computer science; STAR model; Sample (material); Mathematics; Algorithm; Autoregressive integrated moving average; Statistics; Data mining; Artificial intelligence; Time series; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003012024,0.0001745995,0.0002668414,0.00005560236,0.0001813938,0.0002326437,0.00005443523,0.0001691265,0.00001495865],"category_scores_gemma":[0.00001460775,0.0001478412,0.00003688285,0.0002935322,0.00001487445,0.0002375272,0.00001437413,0.0002562023,0.00001151984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004893553,"about_ca_system_score_gemma":0.0000326355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002771795,"about_ca_topic_score_gemma":0.000005689154,"domain_scores_codex":[0.9988623,0.00004615435,0.0003153994,0.0002505363,0.0002392711,0.0002863713],"domain_scores_gemma":[0.9996303,0.00002372518,0.00004122049,0.00007129888,0.00007741834,0.0001560238],"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":[0.0002514701,0.0002954604,0.0001594676,0.002052374,0.0002439203,0.00005805019,0.0006289702,0.7407472,0.1248799,0.01502286,0.001950346,0.1137099],"study_design_scores_gemma":[0.0003480216,0.00003645052,0.000002241021,0.0000510436,0.0000179988,0.0005527819,0.0002045305,0.9924462,0.0001082205,0.0001576529,0.005878639,0.0001961493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01489508,0.0009598076,0.9760506,0.00005833341,0.0001382345,0.0002388215,9.681426e-7,0.0004918585,0.007166302],"genre_scores_gemma":[0.9971455,0.00001771875,0.001886802,0.0000382326,0.0002791502,0.00008018,0.000001236212,0.00003308249,0.0005180889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9822505,"threshold_uncertainty_score":0.6028789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02649120868850038,"score_gpt":0.249239142722078,"score_spread":0.2227479340335776,"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."}}