{"id":"W2176472020","doi":"10.1016/j.electacta.2015.09.058","title":"Quantifying Percolation Events in PEM Fuel Cell Using Synchrotron Radiography","year":2015,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Research Council Canada; Western Economic Diversification Canada; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Light Source","keywords":"Electrolyte; Synchrotron; Proton exchange membrane fuel cell; Percolation (cognitive psychology); Membrane; Diffusion; Fuel cells; Gaseous diffusion; Water transport; Porosity; Flow (mathematics); Materials science; Chemistry; Chemical engineering; Mechanics; Water flow; Composite material; Environmental science; Thermodynamics; Electrode; Physics; Optics; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003961433,0.0002287144,0.0002813763,0.000791668,0.0004143092,0.0007085287,0.000503739,0.000615735,0.001491036],"category_scores_gemma":[0.001050833,0.0003986337,0.0001787274,0.0005697482,0.0005593013,0.0008953754,0.0004020475,0.0005869911,0.0002463702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005194618,"about_ca_system_score_gemma":0.0002826807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009839016,"about_ca_topic_score_gemma":0.001123883,"domain_scores_codex":[0.9997957,0.00003808882,0.000008013714,0.0000465778,0.00007198635,0.00003963746],"domain_scores_gemma":[0.999485,0.0002673105,0.00006829532,0.00004327668,0.00009782387,0.00003830341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005637704,0.0001429087,0.007482553,0.0001239561,0.00003501095,0.0004646342,0.0002471384,0.005616989,0.9748743,0.001266255,0.0003626469,0.008819866],"study_design_scores_gemma":[0.0000445552,0.000379943,0.04062601,0.00002186913,0.00005259116,0.0005739196,0.0005568805,0.08178695,0.873261,0.001069121,0.001590154,0.00003704117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788709,0.0008656931,0.01755656,0.00008790713,0.00001356377,0.00003828906,0.0001250732,0.000210421,0.002231762],"genre_scores_gemma":[0.9948837,0.0002653463,0.004233904,0.00001796542,0.000005066862,0.00001412141,0.00005724353,0.00001702847,0.0005055655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001491036,"threshold_uncertainty_score":0.004988015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519632555404464,"score_gpt":0.2344623329224573,"score_spread":0.2092660073684127,"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."}}