{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001648208,0.000288393,0.0003167924,0.0009599095,0.0003012976,0.00007049226,0.0008494936,0.00009636692,0.00002731741],"category_scores_gemma":[0.0002963979,0.0002539532,0.00004483685,0.00452681,0.0004511746,0.0001974497,0.00005209928,0.0004658164,0.00004579596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004822315,"about_ca_system_score_gemma":0.0001744058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002389767,"about_ca_topic_score_gemma":0.00007366361,"domain_scores_codex":[0.9968667,0.00005620268,0.0004135384,0.0006431469,0.001153635,0.0008667663],"domain_scores_gemma":[0.9988106,0.00003056904,0.00009053379,0.0006239293,0.00029402,0.0001503545],"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":[0.00001800559,0.0003502328,0.01267863,0.00001314145,0.00001339281,0.000002931073,0.00001377026,0.0001944225,0.9583405,0.00058863,0.0001696272,0.0276167],"study_design_scores_gemma":[0.001432656,0.007063639,0.05061134,0.00004016367,0.00009272526,0.00002392033,0.00004061761,0.5834529,0.3500646,0.001139519,0.005083574,0.0009543668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8563103,0.0004299516,0.1352005,0.001009057,0.0009404329,0.001263954,0.000005228613,0.002507732,0.002332881],"genre_scores_gemma":[0.9977611,0.000007951402,0.001958495,0.00008370273,0.00007064934,0.00007520077,0.000001703493,0.00002961571,0.00001160752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6082759,"threshold_uncertainty_score":0.9999913,"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."}}