{"id":"W1875240552","doi":"","title":"PEM Fuel Cell Modelling Using Artificial Neural Networks","year":2014,"lang":"en","type":"article","venue":"DergiPark (Istanbul University)","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Proton exchange membrane fuel cell; Transient (computer programming); Artificial neural network; Fuel cells; Chemical energy; Voltage; Unitized regenerative fuel cell; Energy (signal processing); Engineering; Electric potential energy; Automotive engineering; Nuclear engineering; Computer science; Electrical engineering; Chemistry; Chemical engineering; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00009610763,0.0002023534,0.0002186926,0.0001558395,0.0001482216,0.00006920037,0.0001948692,0.0001889034,0.00008669623],"category_scores_gemma":[0.000002358889,0.0002312435,0.00008946869,0.0002824489,0.00004421126,0.0001673163,0.00004938677,0.000210455,0.00002438896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009795956,"about_ca_system_score_gemma":0.00001056376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003291334,"about_ca_topic_score_gemma":0.000008739074,"domain_scores_codex":[0.9990517,0.00005104429,0.0001790288,0.0002242503,0.000111664,0.0003823506],"domain_scores_gemma":[0.9995307,0.00004413419,0.00004669235,0.0002181032,0.00003746158,0.0001229308],"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":[0.00002463484,0.00001454843,0.000006093678,0.0001688562,0.00001879557,0.00004865326,0.00009462382,0.9971203,0.001078896,0.001267132,0.0001105519,0.00004691531],"study_design_scores_gemma":[0.000240375,0.00001722164,0.000001601483,0.00001559789,0.00005291621,0.000004814267,0.0001351606,0.9699457,0.0006114263,0.0003511931,0.02834892,0.0002750644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4813566,0.001158729,0.3438588,0.00002287174,0.002221666,0.0001985732,0.00001116588,0.0006942984,0.1704773],"genre_scores_gemma":[0.9970422,0.0003364818,0.001850619,0.00001605459,0.0002223456,1.479948e-7,0.00001134311,0.00004509873,0.000475644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5156857,"threshold_uncertainty_score":0.9429834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173743135337875,"score_gpt":0.1553054704673239,"score_spread":0.1435680391139452,"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."}}