{"id":"W2804432531","doi":"10.3389/fncom.2018.00040","title":"Modeling Current Sources for Neural Stimulation in COMSOL","year":2018,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Duke University","keywords":"Multiphysics; Electrode; Computer science; Substrate (aquarium); Materials science; Current source; Current (fluid); Finite element method; Electrode array; Silicone; Biomedical engineering; Electronic engineering; Optoelectronics; Biological system; Electrical engineering; Chemistry; Physics; Engineering; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413204,0.0001841813,0.0001869221,0.0005237897,0.0002471031,0.0001147929,0.0005106918,0.00003407694,0.00000158038],"category_scores_gemma":[0.0007533503,0.0001905803,0.00005186028,0.001245588,0.0003083891,0.0006983823,0.00009092378,0.0002125553,0.000003101205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006626407,"about_ca_system_score_gemma":0.00006438523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004490451,"about_ca_topic_score_gemma":0.00000360072,"domain_scores_codex":[0.9977716,0.00007421823,0.0003880049,0.0007882665,0.0004699325,0.0005079514],"domain_scores_gemma":[0.9994361,0.0002120986,0.00006738154,0.0001464326,0.00004743645,0.0000905448],"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":[0.00003691358,0.00006168252,0.003167475,0.00001126206,7.342462e-8,0.000005511303,0.000104539,0.9612163,0.03176123,0.0006762545,0.00005314681,0.002905666],"study_design_scores_gemma":[0.0004918795,0.0001246837,0.003743347,0.00002532925,0.000001122484,0.000009753775,0.000012437,0.98493,0.00596435,0.004257063,0.0002479236,0.0001920537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896504,0.00002719191,0.3068011,0.0002196041,0.002854409,0.0003586737,0.000009212415,0.00005841979,0.00002106494],"genre_scores_gemma":[0.9954855,0.00001092725,0.003485945,0.0008306894,0.0001125381,0.00003881307,0.000001867047,0.00001757987,0.00001607156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3058352,"threshold_uncertainty_score":0.7771638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06464651854647363,"score_gpt":0.3203746748439333,"score_spread":0.2557281562974597,"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."}}