{"id":"W2921563978","doi":"10.1016/j.automatica.2019.01.035","title":"Identification of symmetric noncausal processes","year":2019,"lang":"en","type":"article","venue":"Automatica","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Honeywell (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimation theory; Identification (biology); System identification; Covariance; Applied mathematics; Process (computing); Maximum likelihood; Covariance matrix; Mathematical optimization; Algorithm; Control theory (sociology); Computer science; Statistics; Data modeling; Artificial intelligence","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":[0.00007723577,0.00004820906,0.0001037075,0.00009408353,0.000008390493,0.00001405688,0.0000655394,0.00003114714,0.00006385022],"category_scores_gemma":[0.00005023426,0.0000451585,0.00001952408,0.0003616784,0.000005790594,0.00006922275,0.000003990325,0.00002964511,0.0005696955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001373969,"about_ca_system_score_gemma":0.000009260878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002924934,"about_ca_topic_score_gemma":0.000001175086,"domain_scores_codex":[0.9995409,0.000007750216,0.0002153467,0.00005613808,0.0001087059,0.00007113031],"domain_scores_gemma":[0.9997274,0.00004459828,0.00003746551,0.0001366507,0.00003608038,0.00001778003],"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":[0.00001787062,0.0001659656,0.004756762,0.01077134,0.0003873942,0.000002944829,0.001927141,0.01859605,0.8844505,0.00973076,0.003430075,0.06576318],"study_design_scores_gemma":[0.0003629867,0.0000395594,0.007918503,0.0000643226,0.00001874287,0.000006516133,0.0001240229,0.9308727,0.05713666,0.0002048951,0.003109949,0.0001411288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835947,0.0002108268,0.001547566,0.00001946606,0.0004785061,0.0002177774,0.000004031294,0.0004304906,0.0134966],"genre_scores_gemma":[0.9995218,0.000005560087,0.0000385669,0.000004835705,0.00001834405,0.00001635474,0.000001203213,0.0000101196,0.0003832179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9122766,"threshold_uncertainty_score":0.7322473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004483659233669542,"score_gpt":0.2076584847583842,"score_spread":0.2031748255247147,"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."}}