{"id":"W4387170312","doi":"10.1080/15435075.2023.2262006","title":"Artificial intelligence-assisted optimization and multiphase analysis of polygon PEM fuel cells","year":2023,"lang":"en","type":"article","venue":"International Journal of Green Energy","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Proton exchange membrane fuel cell; Computational fluid dynamics; Fuel efficiency; Power (physics); Hexagonal crystal system; Computer science; Response surface methodology; Artificial neural network; Power density; Materials science; Nuclear engineering; Fuel cells; Automotive engineering; Mathematical optimization; Mathematics; Mechanics; Chemical engineering; Engineering; Chemistry; Artificial intelligence; Thermodynamics; Physics; Machine learning","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":[0.000154275,0.00008986991,0.0002233278,0.0008212904,0.000013605,0.00003214881,0.0001644896,0.00007577175,0.0001386999],"category_scores_gemma":[0.00001428642,0.0000788707,0.0001203381,0.000446548,0.00003052822,0.0001080485,0.00002929986,0.00006242684,0.000002402801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003295602,"about_ca_system_score_gemma":0.00001300602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001805112,"about_ca_topic_score_gemma":0.00004874097,"domain_scores_codex":[0.9990014,0.00002268288,0.0005456342,0.00007214083,0.0002662708,0.00009187638],"domain_scores_gemma":[0.9994143,0.00006119089,0.0001900029,0.00005966992,0.0002188729,0.00005597216],"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":[0.0000455995,0.00002899447,0.00002848878,0.00002200131,0.001171283,0.0000504962,0.0001451498,0.9495363,0.03888451,0.0001788665,0.00008149238,0.009826769],"study_design_scores_gemma":[0.0001426597,0.00005156013,0.000283768,0.00004204953,0.0003012758,0.00002122457,0.00008495383,0.9290603,0.06872279,0.0004587795,0.0007172243,0.0001134277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8109589,0.002756217,0.1737812,0.0007768515,0.009012103,0.00009648883,0.0002136179,0.0001848434,0.002219804],"genre_scores_gemma":[0.9947208,0.003253553,0.001670381,0.00001789549,0.000168086,6.946873e-7,0.00003279596,0.00001494971,0.0001208669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1837619,"threshold_uncertainty_score":0.3216254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639370849951139,"score_gpt":0.2433117289805697,"score_spread":0.2269180204810583,"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."}}