{"id":"W2903216893","doi":"10.1149/ma2018-02/41/1352","title":"Pore-Network Reconstruction and Simulation of the Fuel Cell Catalyst Layer","year":2018,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scale (ratio); Layer (electronics); Catalysis; Computer science; Porosity; Combustion; Materials science; Process engineering; Nanotechnology; Distributed computing; Engineering; Chemistry; Physics; Composite material","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":[0.0001943143,0.0003037812,0.0004927111,0.0003035106,0.0003444287,0.0006130876,0.0008194752,0.001592053,0.00213766],"category_scores_gemma":[0.001073316,0.0002717807,0.0005548217,0.0004181644,0.0006298291,0.0006086561,0.0006433164,0.0006699497,0.0002336826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006450613,"about_ca_system_score_gemma":0.0006896888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156023,"about_ca_topic_score_gemma":0.005258462,"domain_scores_codex":[0.999884,0.00002217543,0.000005656036,0.00002272322,0.00003155975,0.00003385805],"domain_scores_gemma":[0.9995939,0.0002488296,0.00003789676,0.0000305586,0.00005244441,0.00003644436],"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":[0.00003124115,0.00001923924,0.0008923172,0.00003724446,0.00001031414,0.0000652349,0.00002671868,0.9921083,0.002162404,0.003379912,0.0001954356,0.001071696],"study_design_scores_gemma":[0.000004083538,0.00000312433,0.0001130092,0.000002074032,0.000001346437,0.000004896182,0.000006924651,0.9989189,0.0003296751,0.0004467255,0.0001670153,0.00000220412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7941554,0.0008945544,0.1756728,0.00129039,0.0001359735,0.00009452717,0.002077687,0.001000811,0.02467791],"genre_scores_gemma":[0.9787747,0.0002015548,0.01867872,0.0000671468,0.00001148444,0.00006741487,0.0005633986,0.000068226,0.001567501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01156023,"threshold_uncertainty_score":0.02298588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01513425058814551,"score_gpt":0.2499630388503222,"score_spread":0.2348287882621767,"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."}}