{"id":"W3112714362","doi":"10.1109/smc42975.2020.9283266","title":"Tissue Discrimination from Impedance Spectroscopy as a Multi-objective Optimisation Problem with Weighted Naïve Bayes Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Electrical impedance; Dielectric spectroscopy; Naive Bayes classifier; Computer science; Artificial intelligence; Sorting; Pattern recognition (psychology); Bayes' theorem; Genetic algorithm; Mathematics; Biological system; Algorithm; Bayesian probability; Machine learning; Engineering; Chemistry; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00003396954,0.0002074664,0.0001755544,0.00006843368,0.00006405725,0.00005957347,0.0001169122,0.00008680658,0.000105764],"category_scores_gemma":[0.00001092953,0.000158382,0.00003872834,0.0006096476,0.00003553148,0.0003008994,0.00001090943,0.0001815601,0.00008409794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007609265,"about_ca_system_score_gemma":0.0000214487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001253294,"about_ca_topic_score_gemma":0.00008332581,"domain_scores_codex":[0.9989828,0.00002611577,0.0002048848,0.0003408952,0.0002130132,0.0002322926],"domain_scores_gemma":[0.9995852,0.00004344376,0.00005499447,0.0001234781,0.00007707236,0.0001157999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001762804,0.000134688,0.001853955,0.00009414682,0.0001399232,0.000006757175,0.002908421,0.0008101367,0.9395185,0.001674806,0.0007760451,0.05190637],"study_design_scores_gemma":[0.0007601635,0.0004613782,0.01109044,0.00003564684,0.00005281331,0.000001856221,0.0003372532,0.3396829,0.645978,0.0008464181,0.000382631,0.0003704862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3919612,0.0003556169,0.5961951,0.001476638,0.00006501529,0.0008184531,0.00002860072,0.00119345,0.007905918],"genre_scores_gemma":[0.9198992,0.0001130488,0.07946306,0.0001229892,0.0001186842,0.00007856004,0.0001029908,0.00003211685,0.00006932631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.527938,"threshold_uncertainty_score":0.6458632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138469594512281,"score_gpt":0.2330422581064558,"score_spread":0.2191952986552277,"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."}}