{"id":"W2081174956","doi":"10.1049/iet-smt.2013.0087","title":"Pilot study: electrical impedance based tissue classification using support vector machine classifier","year":2014,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Support vector machine; Classifier (UML); Computer science; Pattern recognition (psychology); Artificial intelligence; Electrical impedance; Engineering; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001449298,0.0004179225,0.0006064562,0.0004791442,0.000235841,0.0004416889,0.000593263,0.0006505143,0.002947414],"category_scores_gemma":[0.002613129,0.0001309544,0.0003689837,0.0003260386,0.0002616539,0.0005812349,0.0002917921,0.00037429,0.0008301249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724196,"about_ca_system_score_gemma":0.0003322623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001445633,"about_ca_topic_score_gemma":0.001144893,"domain_scores_codex":[0.9994615,0.0001741616,0.00004616804,0.0001085161,0.0001546654,0.00005508433],"domain_scores_gemma":[0.9979646,0.0006853538,0.00005884992,0.0001872665,0.0009820516,0.0001218129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006038478,0.008802862,0.09138504,0.0008126671,0.0002729953,0.001759315,0.0009681071,0.02794007,0.2529441,0.001298506,0.01004973,0.5977282],"study_design_scores_gemma":[0.001417377,0.0513817,0.1621059,0.0001355271,0.0004056138,0.003679617,0.001817445,0.5492571,0.2130558,0.001343788,0.01522905,0.0001710515],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9368575,0.000243554,0.05854954,0.0004354986,0.0001720204,0.0007968332,0.0006080637,0.0005753738,0.00176164],"genre_scores_gemma":[0.944312,0.000169407,0.0507082,0.0001345544,0.00006374646,0.0004757103,0.0008334079,0.00002748828,0.00327547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002947414,"threshold_uncertainty_score":0.009860098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0507070464895925,"score_gpt":0.2702244605377198,"score_spread":0.2195174140481272,"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."}}