{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002962723,0.0003264887,0.0002920957,0.0004563559,0.0001387637,0.0001064995,0.000355077,0.0001917352,0.00002098993],"category_scores_gemma":[0.00005009045,0.0003098163,0.0002285954,0.001421294,0.00006519879,0.000195395,0.000008859509,0.0004474649,0.0001455771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007342548,"about_ca_system_score_gemma":0.00002021947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008963846,"about_ca_topic_score_gemma":0.000003647441,"domain_scores_codex":[0.998054,0.00005586406,0.000387961,0.0004586308,0.0004272176,0.0006162975],"domain_scores_gemma":[0.9990019,0.0001218878,0.00006169998,0.0005444711,0.00007525447,0.0001948529],"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":[0.0001544654,0.001048767,0.002778299,0.0003651021,0.0002622878,0.00002623409,0.0002700399,0.3046394,0.2245246,0.02252475,0.05897404,0.384432],"study_design_scores_gemma":[0.0003497804,0.0001115912,0.001481931,0.00003349426,0.00002619929,0.000001950593,0.000001360888,0.9489214,0.04397372,0.001767987,0.002926657,0.0004039512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08881693,0.000768746,0.890159,0.0003858056,0.0007793781,0.0005923262,0.00006460759,0.002127785,0.01630538],"genre_scores_gemma":[0.9892489,0.0000478268,0.009560378,0.0004271624,0.0002707121,0.00009913028,0.00004126348,0.0000654207,0.0002391641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.900432,"threshold_uncertainty_score":0.9999354,"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."}}