{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001314383,0.0004829043,0.0002528177,0.0002457141,0.0002341588,0.000387678,0.0004502905,0.0003141569,0.002012177],"category_scores_gemma":[0.0002281003,0.0001804014,0.0002166196,0.000320565,0.0002060561,0.0003944045,0.0003925487,0.0006465588,0.0006571913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825951,"about_ca_system_score_gemma":0.0002728223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001188062,"about_ca_topic_score_gemma":0.003753208,"domain_scores_codex":[0.9998003,0.000007314564,0.00001174937,0.00006038194,0.00008579632,0.00003438931],"domain_scores_gemma":[0.9999366,0.00001073151,0.000008503732,0.00001255618,0.00002283497,0.000008738714],"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":[0.00002741387,0.00001708762,0.0001986561,0.0001457186,0.00000818657,0.00006125008,0.0000219582,0.00008106955,0.9945741,0.0005064938,0.0002182044,0.004139871],"study_design_scores_gemma":[0.000002892019,0.00002984249,0.0003375233,0.000001844217,0.000003144092,0.00005747515,0.000006161907,0.0004051918,0.9964904,0.00002839071,0.002634784,0.000002379981],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9498752,0.00201364,0.02494168,0.000292706,0.0001944231,0.0001214421,0.0006650459,0.0003853429,0.02151046],"genre_scores_gemma":[0.9797391,0.001150146,0.008460429,0.00006630358,0.00002110897,0.0000394242,0.0005488166,0.0000843361,0.009890318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002012177,"threshold_uncertainty_score":0.006731331,"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."}}