{"id":"W2190284196","doi":"10.1007/s00500-015-1965-1","title":"Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation","year":2015,"lang":"en","type":"article","venue":"Soft Computing","topic":"Control Systems and Identification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gene expression programming; Partial differential equation; Identification (biology); Inverse problem; First-order partial differential equation; Mathematics; Computer science; Inverse; Expression (computer science); Differential equation; Mathematical optimization; Applied mathematics; Mathematical analysis; Biology; Artificial intelligence; Geometry; Programming language","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":[0.0002924715,0.0001218807,0.0001890207,0.0001004663,0.00005607225,0.00009272265,0.0001213558,0.00008996066,0.000001647702],"category_scores_gemma":[0.0002163151,0.0001225469,0.00007127169,0.0001852222,0.00001408721,0.000145704,0.00002735725,0.00006958625,0.00000465162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005866249,"about_ca_system_score_gemma":0.00003510922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006206698,"about_ca_topic_score_gemma":0.000002906788,"domain_scores_codex":[0.9987893,0.00004480927,0.0005265936,0.0002308379,0.0002127238,0.0001957375],"domain_scores_gemma":[0.9992515,0.00006210573,0.0002105609,0.0002292965,0.0001517974,0.00009469385],"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":[0.00007150121,0.000147572,0.001034888,0.000518134,0.00007537693,2.036796e-7,0.003180105,0.2597924,0.6296159,0.0001745038,0.00300002,0.1023894],"study_design_scores_gemma":[0.0009281759,0.00002804791,0.0005613294,0.00004894666,0.00003266618,9.938791e-7,0.00008608145,0.971489,0.02606679,0.0001384781,0.000488714,0.0001307643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2306259,0.00006433803,0.7679889,0.000007678314,0.0003884227,0.0007368988,0.000002678524,0.0001630906,0.00002208874],"genre_scores_gemma":[0.9749417,0.000001134025,0.02435003,0.000001356266,0.0003546813,0.00006465469,0.0001923376,0.00002516065,0.00006897802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7443158,"threshold_uncertainty_score":0.4997316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06234497859373928,"score_gpt":0.252029835010993,"score_spread":0.1896848564172537,"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."}}