{"id":"W2942517617","doi":"10.26434/chemrxiv.8081777","title":"Direct CO2 Electroreduction from Carbonate","year":2019,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electrolysis; Syngas; Electrolyte; Carbonate; Upgrade; Chemical engineering; Electrochemistry; Waste management; Potassium carbonate; Polymer electrolyte membrane electrolysis; Chemistry; Catalysis; Process engineering; Materials science; Electrode; Computer science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011707,0.000362772,0.0004779789,0.0001241244,0.00004855888,0.0000749742,0.0003783817,0.0005473337,0.0007564009],"category_scores_gemma":[0.00002771239,0.0003765857,0.000312971,0.0001449918,0.00005179081,0.00005612373,0.0003284453,0.0006580318,0.0002805199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002659118,"about_ca_system_score_gemma":0.0001281972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005429324,"about_ca_topic_score_gemma":0.00005657274,"domain_scores_codex":[0.998262,0.0000447695,0.00032625,0.0008024053,0.0002544538,0.0003101192],"domain_scores_gemma":[0.9984602,0.00002609526,0.0002324128,0.001094502,0.00009935984,0.00008744546],"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":[0.0004204896,0.0005254066,0.0006410321,0.0005604148,0.00172062,0.0000396405,0.001098192,0.009449336,0.7763615,0.006483996,0.1456093,0.05709005],"study_design_scores_gemma":[0.0002067016,0.00004125167,0.0004287352,0.0001089152,0.0001425978,0.000006790935,0.00002881431,0.0006013954,0.8399117,0.01046966,0.1474075,0.0006459553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8236758,0.002676057,0.00018569,0.0003852284,0.003912047,0.0004248323,0.00001922166,0.001256483,0.1674647],"genre_scores_gemma":[0.9860333,0.000520058,0.0002689652,0.00006107704,0.001239362,0.0001415696,0.0008775328,0.00008540119,0.01077276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1623575,"threshold_uncertainty_score":0.9998686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126440474891284,"score_gpt":0.2395891684269222,"score_spread":0.2269451209377938,"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."}}