{"id":"W2352480763","doi":"","title":"Electrical Capacitance Tomography Identification Algorithm Based on GMM Model","year":2014,"lang":"en","type":"article","venue":"Harbin Ligong Daxue xuebao","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Electrical capacitance tomography; Mixture model; Capacitance; Gaussian; Algorithm; Computer science; Artificial intelligence; Pattern recognition (psychology); Identification (biology); Tomography; Physics","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":[0.0004937732,0.0007889986,0.0007350522,0.001097722,0.0005758308,0.0008608818,0.0009249561,0.0009374518,0.002581096],"category_scores_gemma":[0.001543219,0.0003407479,0.0007909777,0.0009502066,0.0004005462,0.001389911,0.0007587644,0.0008958593,0.001196758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006570469,"about_ca_system_score_gemma":0.001063469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007027021,"about_ca_topic_score_gemma":0.003824562,"domain_scores_codex":[0.9994831,0.00006829637,0.00002670686,0.0001677736,0.0001985947,0.00005545269],"domain_scores_gemma":[0.9996617,0.00007183337,0.00002870225,0.00002966463,0.0001882409,0.00001977086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001615729,0.00005250329,0.002652453,0.0001469143,0.00007107293,0.0002037551,0.0002220744,0.1759706,0.03245023,0.01790852,0.00909947,0.7610608],"study_design_scores_gemma":[0.00001122353,0.00002766397,0.0008468588,0.00001104445,0.00001910877,0.0002271213,0.00003736448,0.9791359,0.009043539,0.005566737,0.005041881,0.00003155086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00316944,0.0001198772,0.9947588,0.00007836401,0.00003785632,0.00002556535,0.00003070049,0.0008065567,0.0009728199],"genre_scores_gemma":[0.3195157,0.0008416193,0.6687989,0.000238398,0.000138248,0.000262592,0.0005536447,0.0003570585,0.009293816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007027021,"threshold_uncertainty_score":0.01397228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005101972829197663,"score_gpt":0.1850699432431498,"score_spread":0.1799679704139522,"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."}}