{"id":"W2900266296","doi":"10.1016/j.joule.2018.10.021","title":"Boosting the Single-Pass Conversion for Renewable Chemical Electrosynthesis","year":2018,"lang":"en","type":"article","venue":"Joule","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Electrosynthesis; Renewable energy; Process engineering; Materials science; Engineering; Computer science; Chemistry; Electrical engineering; Artificial intelligence; Electrochemistry","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":[0.0001599462,0.00007483504,0.00009078832,0.00002313642,0.0001812556,0.00003276055,0.0001393873,0.00006972121,0.0002573502],"category_scores_gemma":[0.0001187831,0.00005417044,0.00008138393,0.0001083359,0.00006082949,0.00005615522,0.00003746456,0.00005358545,0.00004050592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007092929,"about_ca_system_score_gemma":0.00001763973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003071098,"about_ca_topic_score_gemma":0.00003732075,"domain_scores_codex":[0.999408,0.00001504765,0.0001238959,0.0001538021,0.00009524298,0.0002039802],"domain_scores_gemma":[0.9995268,0.00008177695,0.00005946457,0.0002192855,0.00007845027,0.00003420454],"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":[0.00004917963,0.00003604799,0.00001236199,0.00001008657,0.00001649617,2.968566e-7,0.00008076549,0.000002699612,0.9082325,0.001397654,0.06280324,0.02735868],"study_design_scores_gemma":[0.00007221286,0.00005537222,0.000008184317,0.00000710099,0.0000127057,0.000006115311,0.00004256719,0.0003490842,0.6490558,0.001194873,0.3491416,0.00005444755],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320268,0.0002940581,0.01375679,0.007839365,0.001062477,0.000747344,0.00001390447,0.001038656,0.1432206],"genre_scores_gemma":[0.99257,0.000006478238,0.001871256,0.0002838373,0.0007207114,0.00003601212,0.00000888608,0.00001967197,0.00448312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2863383,"threshold_uncertainty_score":0.2817805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921108241578987,"score_gpt":0.2469421191519629,"score_spread":0.227731036736173,"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."}}