{"id":"W2161197940","doi":"10.1002/aic.12112","title":"Robust identification of piecewise/switching autoregressive exogenous process","year":2009,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Control Systems and Identification","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Expectation–maximization algorithm; Autoregressive model; Algorithm; Mathematical optimization; Computer science; Maximization; Identification (biology); Mathematics; Artificial intelligence; Maximum likelihood","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":[0.0003939521,0.00009795795,0.0001624135,0.0001219322,0.0001011337,0.000101543,0.0001688834,0.00005962131,0.00001838722],"category_scores_gemma":[0.00004856688,0.00008960244,0.00006748779,0.0001221189,0.00000704082,0.0003270491,0.00000357422,0.0001960221,0.00001507296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006644955,"about_ca_system_score_gemma":0.0000290656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004646259,"about_ca_topic_score_gemma":0.000004198345,"domain_scores_codex":[0.9989838,0.00002391117,0.0005168857,0.00009072608,0.0002270028,0.0001576685],"domain_scores_gemma":[0.9993238,0.0000137254,0.0002504431,0.0001586218,0.0001882993,0.00006508844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001605014,0.00007450608,0.0006424059,0.00009515142,0.00009081502,0.00001131327,0.003406616,0.107683,0.8031367,0.0001919039,0.001515235,0.08313628],"study_design_scores_gemma":[0.00289475,0.0002882747,0.331903,0.0008223235,0.0003470363,0.001177153,0.002281096,0.4811501,0.166939,0.008712287,0.002339873,0.00114519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9016987,0.002243991,0.09381086,0.000201285,0.0008045636,0.0001798235,0.00000312848,0.000115329,0.0009422831],"genre_scores_gemma":[0.9993541,0.0000694306,0.0001198044,0.00001086173,0.0003063566,0.000003377025,0.000002473126,0.00001336951,0.0001202748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6361977,"threshold_uncertainty_score":0.3653882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163359998158322,"score_gpt":0.2201232247965853,"score_spread":0.2084896248150021,"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."}}