{"id":"W2945075429","doi":"10.1021/acsenergylett.9b00975","title":"CO <sub>2</sub> Electroreduction from Carbonate Electrolyte","year":2019,"lang":"en","type":"article","venue":"ACS Energy Letters","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":260,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Syngas; Electrolysis; Electrolyte; Carbonate; Electrochemistry; Chemical engineering; Catalysis; Upgrade; Chemistry; Materials science; Waste management; Electrode; Organic chemistry; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008689953,0.0002839633,0.0002790093,0.000187688,0.00008512671,0.00005673002,0.0002687733,0.0001578311,0.0001470315],"category_scores_gemma":[0.000007433178,0.0002966525,0.0001623346,0.0003182807,0.00005214657,0.0002217301,0.00003023612,0.0002459144,0.0002024507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002063786,"about_ca_system_score_gemma":0.00003667959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003122142,"about_ca_topic_score_gemma":0.0001715951,"domain_scores_codex":[0.9982277,0.00007495704,0.0002938106,0.0005563998,0.0003262128,0.0005209928],"domain_scores_gemma":[0.9991003,0.00003241847,0.0001387966,0.0005853545,0.00004375166,0.00009936856],"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":[0.00005481558,0.00002544441,0.00005539072,0.000002872708,0.00009542709,0.000007736982,0.00003922997,0.0004827344,0.9597963,0.005293428,0.01693231,0.01721428],"study_design_scores_gemma":[0.0003125991,0.00005995006,0.0002660434,0.000009193174,0.00003165436,0.00002239006,0.00001371246,0.00009859,0.9048465,0.0008716538,0.09314362,0.0003240969],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907297,0.0002062689,0.0004854426,0.002041966,0.0007355019,0.00008316909,0.000005846962,0.0004966781,0.005215417],"genre_scores_gemma":[0.994382,0.0002230987,0.00007696761,0.003437763,0.0008947264,0.00006112587,0.0003299967,0.00007518216,0.0005191398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07621131,"threshold_uncertainty_score":0.9999486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003870545469062706,"score_gpt":0.1927894274187706,"score_spread":0.1889188819497079,"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."}}