{"id":"W2948631477","doi":"10.11159/ehst19.1","title":"Bottom-up Thermal Plasma Approach for Graphene: Tuning catalyst materials for PEM-FC","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Energy Harvesting, Storage, and Transfer","topic":"Graphene research and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Graphene; Plasma; Materials science; Thermal; Catalysis; Nanotechnology; Chemistry; Physics; Thermodynamics; Nuclear physics","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":[0.0001097493,0.0004168873,0.0003375552,0.0002725677,0.0003932328,0.0006102799,0.000619518,0.0006420054,0.002819014],"category_scores_gemma":[0.0001727426,0.0002018908,0.0003570885,0.000217862,0.0003543107,0.0006446203,0.0005403277,0.001109993,0.0009184596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003746879,"about_ca_system_score_gemma":0.0001840715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006032343,"about_ca_topic_score_gemma":0.001256158,"domain_scores_codex":[0.9998388,0.000008720377,0.000003941994,0.00003566367,0.00008176064,0.00003102328],"domain_scores_gemma":[0.9999597,0.00001078425,0.000004247362,0.00001064688,0.000009635505,0.000004858098],"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":[0.00006417555,0.00002940621,0.00005912766,0.0001749753,0.0000155502,0.00004957906,0.00004752201,0.0006260275,0.9869666,0.001171985,0.0007244211,0.01007065],"study_design_scores_gemma":[0.000004827813,0.00005762212,0.0002016163,0.000004995278,0.000009166735,0.00003900277,0.00002852464,0.003431154,0.9911441,0.0003817885,0.004689268,0.000007909548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8335947,0.01060031,0.1112255,0.001734959,0.0008314711,0.0001765726,0.001303219,0.001787708,0.0387455],"genre_scores_gemma":[0.9644946,0.001805312,0.02465277,0.00022655,0.0000484055,0.00006162212,0.0003040317,0.0001340954,0.00827261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002819014,"threshold_uncertainty_score":0.009430528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213210493222721,"score_gpt":0.2537809478607569,"score_spread":0.2216488429285297,"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."}}