{"id":"W4390535844","doi":"10.1016/j.matt.2023.12.008","title":"Catalyst design for electrochemical CO2 reduction to ethylene","year":2024,"lang":"en","type":"article","venue":"Matter","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ethylene; Catalysis; Reduction (mathematics); Electrochemistry; Chemistry; Organic chemistry; Electrode; Mathematics","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":[0.0003650819,0.000363602,0.0004759101,0.000370357,0.0003309273,0.0008550073,0.0007868269,0.0005716313,0.001225842],"category_scores_gemma":[0.0005725196,0.0003040258,0.0002604081,0.0003344868,0.000164625,0.0006131428,0.0002757567,0.0004528849,0.0008067606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006259696,"about_ca_system_score_gemma":0.0004584437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005934434,"about_ca_topic_score_gemma":0.002015922,"domain_scores_codex":[0.9997813,0.00001937654,0.00001716697,0.00004629786,0.0001046773,0.00003126217],"domain_scores_gemma":[0.9999074,0.00001592482,0.00001407284,0.00001062926,0.00004188068,0.0000101523],"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":[0.0002894423,0.0001669037,0.0005610177,0.0005991673,0.00003868675,0.0001177697,0.00005991921,0.009147657,0.9284419,0.009681659,0.0007861549,0.05010978],"study_design_scores_gemma":[0.00004058448,0.0002641695,0.0004199901,0.00002028194,0.00003748125,0.0001228969,0.00003695178,0.0289238,0.9527416,0.0008710785,0.01650324,0.00001803304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5985466,0.01235483,0.358916,0.0007716481,0.0003354171,0.0004038527,0.0005653839,0.0007497349,0.02735644],"genre_scores_gemma":[0.8923018,0.003539025,0.09601755,0.0001040208,0.00003536164,0.0002515015,0.0004162755,0.0001322704,0.007202302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001225842,"threshold_uncertainty_score":0.004541755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619758698372586,"score_gpt":0.2694086010551516,"score_spread":0.2532110140714257,"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."}}