{"id":"W4239881464","doi":"10.26434/chemrxiv-2021-qgprm","title":"Engineering a Non-Natural Photoenzyme for Improved Photon Efficiency","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Radical Photochemical Reactions","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Division of Chemistry; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Catalysis; Protein engineering; Absorption (acoustics); Photon; Function (biology); Flavin group; Excited state; Quantum dot; Biochemical engineering; Chemistry; Materials science; Nanotechnology; Photochemistry; Combinatorial chemistry; Enzyme; Physics; Organic chemistry; Atomic physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001151278,0.0005713798,0.0006698994,0.00007048827,0.00008421377,0.0001749702,0.000716948,0.0007876061,0.0006915503],"category_scores_gemma":[0.001093993,0.0006111089,0.0007125816,0.0001687352,0.000078139,0.00006643104,0.0005239647,0.00174412,0.00001474025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004203726,"about_ca_system_score_gemma":0.0002809073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006772188,"about_ca_topic_score_gemma":0.000002853526,"domain_scores_codex":[0.9974365,0.000003835525,0.0005333287,0.001104048,0.0002426974,0.0006795407],"domain_scores_gemma":[0.9978676,0.0003203111,0.0002191205,0.00110798,0.0001834874,0.0003014859],"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":[0.00002764011,0.0001544011,0.000003738398,0.001136283,0.0001178669,0.000007836816,0.00008255854,0.00009784049,0.9970536,0.000006049468,0.001073024,0.0002391386],"study_design_scores_gemma":[0.0005624356,0.00000603894,0.000007729075,0.0003649126,0.00009497518,0.00001028038,0.00003170261,0.1063904,0.8864782,0.00008174888,0.00538773,0.0005838729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887163,0.0007392252,0.003142961,0.0001818815,0.001225699,0.0007819252,0.00005221373,0.0004675425,0.004692253],"genre_scores_gemma":[0.9905945,0.00004604677,0.004422231,0.0001095264,0.0007029789,0.001389011,0.0007139493,0.0001475192,0.001874247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1105755,"threshold_uncertainty_score":0.999634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009399856823236321,"score_gpt":0.2404397646126455,"score_spread":0.2310399077894092,"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."}}