{"id":"W2939066948","doi":"10.3390/sym11040556","title":"System Identification Based on Tensor Decompositions: A Trilinear Approach","year":2019,"lang":"en","type":"article","venue":"Symmetry","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Multilinear map; Nonlinear system; Tensor (intrinsic definition); Computer science; Context (archaeology); Bilinear interpolation; Nonlinear system identification; System identification; Identification (biology); Linear system; Curse of dimensionality; Mathematical optimization; Algorithm; Applied mathematics; Mathematics; Artificial intelligence; Measure (data warehouse); Data mining","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":[0.001286819,0.00142431,0.0009557661,0.001131611,0.0004187105,0.001289125,0.0009881133,0.001168823,0.002482248],"category_scores_gemma":[0.00221818,0.000666736,0.001934699,0.001010329,0.0009580402,0.001762094,0.001282902,0.002098592,0.001024546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000714951,"about_ca_system_score_gemma":0.001054867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004196461,"about_ca_topic_score_gemma":0.002967902,"domain_scores_codex":[0.9993547,0.0002392453,0.00005285478,0.0001174835,0.0001771591,0.00005856107],"domain_scores_gemma":[0.9989513,0.0004208731,0.0001644484,0.0001186965,0.0002775735,0.00006705234],"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":[0.00009479038,0.00008080255,0.001089805,0.0004669886,0.0001966164,0.0003133237,0.0004117814,0.6956403,0.01845221,0.1791373,0.002640345,0.1014757],"study_design_scores_gemma":[0.000001913521,0.00001822512,0.00008715513,0.0000137334,0.000009240753,0.00003155306,0.00001275799,0.9847494,0.0007082598,0.01328766,0.001067358,0.00001278596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001045575,0.0002399442,0.9980774,0.00006226717,0.00002157306,0.00001043261,0.00001980832,0.00005531183,0.0004676861],"genre_scores_gemma":[0.2616313,0.003817804,0.7249709,0.000259927,0.0002766853,0.0002505048,0.0004576157,0.0002740963,0.008061229],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004196461,"threshold_uncertainty_score":0.008344054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752935285979861,"score_gpt":0.3011408063701561,"score_spread":0.2736114535103575,"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."}}