{"id":"W2111056014","doi":"10.1109/iembs.2006.260176","title":"Separable Least Squares Identification of Long Memory Block Structured Models: Application to Lung Tissue Viscoelasticity","year":2006,"lang":"en","type":"article","venue":"","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Separable space; Viscoelasticity; Least-squares function approximation; Block (permutation group theory); Identification (biology); Mathematical optimization; Curse of dimensionality; Algorithm; Dimension (graph theory); Constant (computer programming); Computer science; Finite element method; Mathematics; System identification; Applied mathematics; Artificial intelligence; Data modeling; Mathematical analysis; Statistics; Engineering; Materials science; Structural engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00008526638,0.0001340737,0.0001691772,0.00009787836,0.00005373831,0.00004072366,0.0001282767,0.00008083021,0.00002830083],"category_scores_gemma":[0.000008544969,0.0001417562,0.00002567023,0.0001532549,0.00001565861,0.000202286,0.00002656143,0.00005910392,0.00002482874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000517081,"about_ca_system_score_gemma":0.000008572937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004117031,"about_ca_topic_score_gemma":0.0002930176,"domain_scores_codex":[0.9990631,0.00001184077,0.0003932524,0.000189402,0.0001580045,0.0001844215],"domain_scores_gemma":[0.9995905,0.00002146832,0.00004909403,0.0002083347,0.00008165778,0.00004895474],"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":[0.000006737809,0.00001012627,0.00001414983,0.0001045133,0.000006039535,2.094615e-7,0.0000496685,0.6524619,0.3453112,0.00153585,0.0001633223,0.0003362202],"study_design_scores_gemma":[0.00008077146,0.00000731202,0.0005608228,0.00001712124,0.00001915979,0.000001633802,0.00001710819,0.6155664,0.382435,0.001164118,0.00001991829,0.0001106885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4099802,0.00004894289,0.5885422,0.0000121835,0.0001221238,0.0002089721,0.00001242962,0.0001635885,0.0009092862],"genre_scores_gemma":[0.9975584,0.00000367465,0.001981096,0.000007625558,0.000113462,0.00003867313,0.0000284368,0.00002646776,0.0002422051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5875781,"threshold_uncertainty_score":0.578065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005446639737446742,"score_gpt":0.214764440149895,"score_spread":0.2093178004124483,"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."}}