{"id":"W2978285812","doi":"10.1149/09208.0061ecst","title":"Pore-Scale Liquid Water Visualization for Understanding Water Transport in Operating Fuel Cells","year":2019,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wetting; Water transport; Materials science; Electrolyte; Proton exchange membrane fuel cell; Chemical engineering; Polymer; Liquid water; Fuel cells; Cathode; Chemistry; Environmental science; Composite material; Environmental engineering; Water flow; Geology; Electrode; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001393637,0.0001430831,0.0001780143,0.0001085376,0.00007267807,0.00003153901,0.00005664246,0.0001521209,0.0008576667],"category_scores_gemma":[1.970596e-7,0.0001041049,0.0000739932,0.00006391976,0.00001203199,0.0001984489,0.000001274412,0.0001043532,0.00007187098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009273468,"about_ca_system_score_gemma":0.000005998907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001388317,"about_ca_topic_score_gemma":0.00003951398,"domain_scores_codex":[0.9991387,0.00001137911,0.0002913206,0.000165869,0.00007695366,0.0003158522],"domain_scores_gemma":[0.9998159,0.00001339657,0.000007972403,0.000104331,0.00001530557,0.00004313529],"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":[0.0000188794,0.00002349565,0.000005804217,0.0004828819,0.00002061953,0.000001612022,0.002352784,0.4294942,0.5675604,0.00002394476,0.00001023323,0.000005102129],"study_design_scores_gemma":[0.0008448728,0.00008012165,0.000005027997,0.00006460179,0.00003589715,0.000003740598,0.0004648981,0.03826554,0.9551297,0.0002115508,0.004627018,0.0002670707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6182138,0.0001197003,0.3694057,0.0001078812,0.002470626,0.0008672009,0.00004814162,0.0003225136,0.008444373],"genre_scores_gemma":[0.9983519,0.0001608106,0.0006074756,0.00001727635,0.000034837,0.00004073682,0.0001001575,0.00005885371,0.0006279082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3912286,"threshold_uncertainty_score":0.9390849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207724808290016,"score_gpt":0.2102478629835229,"score_spread":0.1981706149006227,"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."}}