{"id":"W4368352556","doi":"10.1016/j.jpowsour.2023.233119","title":"Predicting optimal membrane hydration and ohmic losses in operating fuel cells with machine learning","year":2023,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dielectric spectroscopy; Electrolyte; Proton exchange membrane fuel cell; Artificial neural network; Electrochemistry; Electrode; Materials science; Biological system; Computer science; Chemical engineering; Artificial intelligence; Chemistry; Fuel cells; Engineering","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":[0.0003302828,0.0003913489,0.0003694241,0.0002358044,0.0001938489,0.0003962969,0.0003334844,0.0005475369,0.0005300979],"category_scores_gemma":[0.001564248,0.000267168,0.0002437866,0.0001468166,0.0002691921,0.0006982277,0.0001991919,0.0004574594,0.0001108553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630527,"about_ca_system_score_gemma":0.0003671101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003402682,"about_ca_topic_score_gemma":0.003864501,"domain_scores_codex":[0.9999285,0.00001752646,0.000004203027,0.00002059128,0.00001545461,0.00001378551],"domain_scores_gemma":[0.9993987,0.0004329171,0.00005033887,0.000021776,0.00007472838,0.00002154038],"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":[0.0002004836,0.0001054223,0.005180776,0.00002534372,0.00001587784,0.00002678996,0.0000133739,0.9738649,0.00357359,0.0002050415,0.0001916082,0.01659679],"study_design_scores_gemma":[0.000001841081,0.00001073347,0.0004148251,7.573774e-7,0.000001349922,0.000002130439,0.000002483393,0.998181,0.00124731,0.0001233085,0.00001279228,0.00000152538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9327271,0.0001979519,0.06526036,0.000144251,0.0000208603,0.00002254648,0.0001022886,0.0002314868,0.001293098],"genre_scores_gemma":[0.997474,0.000016597,0.00228909,0.000006546851,0.000002704589,0.000005354714,0.00002834035,0.000005902337,0.0001714446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003402682,"threshold_uncertainty_score":0.006765723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004263124433103067,"score_gpt":0.1830413140745379,"score_spread":0.1787781896414348,"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."}}