{"id":"W4323966571","doi":"10.2139/ssrn.4385447","title":"Predicting Optimal Membrane Hydration and Ohmic Losses in Operating Fuel Cells with Machine Learning","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ohmic contact; Fuel cells; Membrane; Computer science; Artificial intelligence; Chemical engineering; Materials science; Chemistry; Engineering; Nanotechnology","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":[0.0003520202,0.000409804,0.0004160284,0.0002540966,0.0002060532,0.0004490885,0.0003465023,0.000618829,0.0005772447],"category_scores_gemma":[0.001595841,0.0002828629,0.0002464358,0.0001671579,0.0002948244,0.0007286154,0.0002232283,0.0004754791,0.0001307378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004844987,"about_ca_system_score_gemma":0.0003824053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003646391,"about_ca_topic_score_gemma":0.00390966,"domain_scores_codex":[0.9999235,0.00001786088,0.000004163614,0.00002269713,0.00001731946,0.00001452011],"domain_scores_gemma":[0.9994345,0.0004052303,0.00004940189,0.00002082989,0.00006999529,0.00001999765],"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":[0.0001704666,0.00008272938,0.003950671,0.00002588076,0.00001404621,0.00002463552,0.00001335155,0.9759086,0.003654213,0.0002060182,0.0001704493,0.01577885],"study_design_scores_gemma":[0.000001770319,0.000009863639,0.0003693376,8.458404e-7,0.000001244307,0.000002075014,0.000002462883,0.9981686,0.001279428,0.000149318,0.00001340291,0.000001630405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101036,0.0002621239,0.08735425,0.0001632707,0.00002378253,0.00002601246,0.0001103987,0.0002849197,0.001671652],"genre_scores_gemma":[0.9971924,0.00002013822,0.002532652,0.00000699088,0.00000279433,0.000005877537,0.0000282523,0.000006973334,0.000203856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003646391,"threshold_uncertainty_score":0.007250369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003333991722171101,"score_gpt":0.1801161379703739,"score_spread":0.1767821462482028,"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."}}