{"id":"W4412534053","doi":"10.1149/1945-7111/adf262","title":"UTILE-Pore: Deep Learning-Enabled 3D Analysis of Porous Materials in Polymer Electrolyte Membrane-Based Energy Devices","year":2025,"lang":"en","type":"article","venue":"Journal of The Electrochemical Society","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Commission; Fuel Cells and Hydrogen Joint Undertaking; HORIZON EUROPE Framework Programme; Forschungszentrum Jülich; Bundesministerium für Bildung und Forschung; Gauss Centre for Supercomputing","keywords":"Electrolyte; Porosity; Materials science; Membrane; Polymer; Chemical engineering; Porous medium; Polymer electrolytes; Nanotechnology; Composite material; Chemistry; Engineering; Electrode; Physical chemistry; Ionic conductivity","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":[0.0003642227,0.0007198574,0.0004522348,0.0008274773,0.0002224098,0.001201627,0.001051953,0.0009629605,0.00257626],"category_scores_gemma":[0.0008071743,0.0004111977,0.0008368463,0.0004058234,0.0004179129,0.0006686086,0.001014886,0.0007630945,0.0006126401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007515487,"about_ca_system_score_gemma":0.0009238999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003444138,"about_ca_topic_score_gemma":0.006200131,"domain_scores_codex":[0.9998666,0.00001547707,0.00000620129,0.00002898726,0.00006390161,0.00001882772],"domain_scores_gemma":[0.9998187,0.00008344059,0.00002019888,0.00002609314,0.00003569194,0.00001598255],"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":[0.0001657345,0.0001681846,0.002729379,0.0003307309,0.0001375836,0.0002181999,0.000128415,0.7416661,0.05385946,0.006530982,0.007301188,0.1867641],"study_design_scores_gemma":[0.000004053768,0.000008675173,0.0002095314,0.000006526985,0.00000354074,0.00003094959,0.000006444045,0.9905042,0.006550036,0.001359919,0.001307546,0.000008417765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04087399,0.0004401766,0.9441941,0.0003236238,0.00004635903,0.00008462771,0.001737897,0.0102583,0.002040977],"genre_scores_gemma":[0.4580169,0.0005678195,0.533738,0.0002898521,0.0000295318,0.0002584663,0.003336408,0.0009087959,0.00285421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003444138,"threshold_uncertainty_score":0.008618474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002365112944102244,"score_gpt":0.1871876193007884,"score_spread":0.1848225063566862,"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."}}