{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002969386,0.0001497129,0.0002187884,0.0002093654,0.0001314225,0.0000643628,0.0002024505,0.00009712711,0.00009762966],"category_scores_gemma":[0.00004610969,0.0001378802,0.0001339416,0.0003927464,0.00002259484,0.00004652544,0.00001613357,0.0001503311,0.001721257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110409,"about_ca_system_score_gemma":0.00002410007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003099586,"about_ca_topic_score_gemma":2.114164e-7,"domain_scores_codex":[0.9987192,0.00008935672,0.0003781932,0.0003525859,0.0002821717,0.0001785258],"domain_scores_gemma":[0.9986249,0.0002876577,0.000162496,0.0007396926,0.0001054677,0.00007982974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005541223,0.0007644504,0.0009204256,0.000460386,0.00004143964,0.000001700255,0.0000638191,0.0002333605,0.005994117,0.9851701,0.005714203,0.0005805522],"study_design_scores_gemma":[0.005357565,0.0003149815,0.01614594,0.0005690584,0.0003249215,0.0001042454,0.002515193,0.9168037,0.02309071,0.02628739,0.007013324,0.001473013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7163197,0.00004252685,0.1395456,0.0008621419,0.0004595119,0.002568809,0.0001845888,0.001297379,0.1387198],"genre_scores_gemma":[0.971801,4.881377e-7,0.02627259,0.0002435507,0.0000843513,0.0001602337,0.00009334231,0.0000341515,0.001310262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9588827,"threshold_uncertainty_score":0.999056,"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."}}