{"id":"W4280553544","doi":"10.1038/s41893-022-00879-8","title":"Carbon-efficient carbon dioxide electrolysers","year":2022,"lang":"en","type":"article","venue":"Nature Sustainability","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":306,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Renewable energy; Carbonate; Fossil fuel; Carbon dioxide; Chemical industry; Environmental science; Electrolysis; Power to gas; Environmental economics; Electricity; Process engineering; Carbon fibers; Biochemical engineering; Waste management; Chemistry; Environmental engineering; Engineering; Computer science","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":[0.0001991148,0.0004120222,0.0003172573,0.0004761397,0.0003306495,0.0007546052,0.0006860658,0.0007592027,0.003964271],"category_scores_gemma":[0.0002984179,0.0002427526,0.0001923346,0.0005702208,0.0002736444,0.001365773,0.0005527813,0.0008620039,0.001511503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004418942,"about_ca_system_score_gemma":0.0002841869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004138596,"about_ca_topic_score_gemma":0.001506564,"domain_scores_codex":[0.9997136,0.00001407096,0.00001265075,0.00006311948,0.0001553276,0.00004129595],"domain_scores_gemma":[0.9999194,0.0000160917,0.000009737059,0.00001352905,0.00002969988,0.00001146964],"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":[0.0001736041,0.00015985,0.000205749,0.00063628,0.00003026091,0.0001458537,0.0000710445,0.0007296211,0.9223211,0.01203003,0.004865688,0.05863102],"study_design_scores_gemma":[0.00001824516,0.00007254492,0.0004908008,0.00001194783,0.00001195058,0.0001223022,0.00002148647,0.002668484,0.961046,0.0006188274,0.03490629,0.00001128052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7615458,0.03938231,0.07754077,0.00295132,0.002421848,0.0002377077,0.001264709,0.002274582,0.112381],"genre_scores_gemma":[0.9383918,0.009084896,0.01589945,0.0003046966,0.000158681,0.00004380221,0.0005545851,0.0001435752,0.03541853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003964271,"threshold_uncertainty_score":0.0132618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002857405865308555,"score_gpt":0.2412132257142156,"score_spread":0.2383558198489071,"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."}}