{"id":"W2770581687","doi":"10.1002/adsu.201770111","title":"Carbon Dioxide Conversion: Tailoring CO<sub>2</sub> Reduction with Doped Silicon Nanocrystals (Adv. Sustainable Syst. 11/2017)","year":2017,"lang":"en","type":"article","venue":"Advanced Sustainable Systems","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dopant; Materials science; Hydride; Nanocrystal; Silicon; Carbon fibers; Doping; Boron; Nanotechnology; Solar fuel; Chemical engineering; Catalysis; Optoelectronics; Photocatalysis; Chemistry; Metallurgy; Composite number; Organic chemistry; 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":[0.0001067826,0.0003799157,0.0001492226,0.0001560286,0.000190659,0.0004403229,0.0001993672,0.0003653289,0.001287604],"category_scores_gemma":[0.00009112047,0.0001718301,0.0002005627,0.0001709028,0.0002941949,0.0003931391,0.0002149238,0.0003321119,0.0005124134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000477937,"about_ca_system_score_gemma":0.000147776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009561832,"about_ca_topic_score_gemma":0.001993055,"domain_scores_codex":[0.9999213,0.000003588208,0.000003609969,0.00002469742,0.0000347622,0.00001195753],"domain_scores_gemma":[0.9999762,0.000005411456,0.000004374289,0.000002203747,0.000007581368,0.000004142876],"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":[0.00004741371,0.00003457665,0.0001207883,0.0001214557,0.00001097787,0.00005649439,0.00002602262,0.0002960439,0.9805676,0.001682989,0.001791595,0.01524389],"study_design_scores_gemma":[0.000007261344,0.00004931822,0.0002607399,0.000003250914,0.000005005945,0.00005923731,0.000008513045,0.001597442,0.9905968,0.0001370214,0.007268894,0.00000668667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8549534,0.02478596,0.03611032,0.002042596,0.001687844,0.0002520285,0.0008270784,0.001202403,0.07813842],"genre_scores_gemma":[0.9482539,0.009802987,0.01828258,0.0004298979,0.0001172924,0.0001107958,0.0005825562,0.0001276331,0.02229235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001287604,"threshold_uncertainty_score":0.004307508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006787966886477689,"score_gpt":0.2096271329896299,"score_spread":0.2028391661031522,"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."}}