{"id":"W2021528888","doi":"10.1109/tnb.2003.820275","title":"Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden markov models","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nernst equation; Markov chain; Computer science; Markov process; Hidden Markov model; Statistical physics; Markov model; Ion; Algorithm; Physics; Biophysics; Biological system; Artificial intelligence; Mathematics; Machine learning; Electrode; Quantum mechanics; Biology","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":[0.001394002,0.0006011225,0.0006482379,0.0005086054,0.000432218,0.0007249913,0.001452453,0.0013026,0.001415945],"category_scores_gemma":[0.005392388,0.0005227682,0.000540978,0.000500257,0.000997007,0.001330521,0.0009144681,0.001356373,0.0002869695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358195,"about_ca_system_score_gemma":0.001070188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006757169,"about_ca_topic_score_gemma":0.0047459,"domain_scores_codex":[0.999785,0.00006673239,0.00001345554,0.00005716989,0.00004898794,0.00002870516],"domain_scores_gemma":[0.9984819,0.001080976,0.0001672595,0.00005373168,0.0001704004,0.00004574322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002109622,0.0000101377,0.0001843309,0.00001456148,0.000008782688,0.00001038426,0.00002567492,0.9740731,0.0004404231,0.007548416,0.0001643661,0.01749883],"study_design_scores_gemma":[0.000002877018,0.000002487341,0.00001616355,0.000001080777,7.320486e-7,0.000001634608,0.000001188384,0.9974557,0.0001157455,0.002364153,0.0000366189,0.000001658213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0182688,0.0001458257,0.9805664,0.0002106886,0.00001561224,0.00002216949,0.00001678204,0.0002196045,0.000534151],"genre_scores_gemma":[0.5585938,0.0002814094,0.437739,0.0001453628,0.00004831575,0.000272463,0.0001145872,0.000163562,0.002641436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006757169,"threshold_uncertainty_score":0.01343566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312830686519014,"score_gpt":0.2409768953031146,"score_spread":0.2178485884379245,"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."}}