{"id":"W1854248613","doi":"10.1007/s11517-015-1355-y","title":"Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms","year":2015,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Robustness (evolution); Computer science; Algorithm; Artificial intelligence; Noise (video); Sensitivity (control systems); Pattern recognition (psychology); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000418213,0.0009138328,0.0008050316,0.0008428941,0.0003203163,0.0008429917,0.0004614431,0.0007691889,0.001415902],"category_scores_gemma":[0.001868178,0.0005084625,0.000970208,0.0007088316,0.0002968506,0.0008078957,0.0004723775,0.0008273898,0.0009970611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003240532,"about_ca_system_score_gemma":0.0008239905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002655657,"about_ca_topic_score_gemma":0.002957179,"domain_scores_codex":[0.9998626,0.00003066134,0.00001071023,0.00002932111,0.00005073816,0.00001594776],"domain_scores_gemma":[0.9995628,0.0002536868,0.00005561292,0.00002790096,0.0000854703,0.00001449137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001344598,0.00008740875,0.001721353,0.0001733356,0.00008963063,0.0001299035,0.00008105997,0.7473175,0.0338965,0.008224236,0.001058285,0.2070864],"study_design_scores_gemma":[0.00000481596,0.000008003855,0.000469118,0.000007588746,0.00001005647,0.0000242008,0.000006968728,0.9954869,0.002136751,0.001470179,0.0003671148,0.00000831801],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009642237,0.00008067688,0.9894285,0.00003779072,0.000007275587,0.00001735139,0.00004384791,0.000258654,0.0004837369],"genre_scores_gemma":[0.3301799,0.0003495792,0.6658956,0.00004669717,0.00003249775,0.0001554183,0.0004721116,0.0003112266,0.002556858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002655657,"threshold_uncertainty_score":0.005280435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06607107724383213,"score_gpt":0.307202157953192,"score_spread":0.2411310807093599,"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."}}