{"id":"W2821134563","doi":"10.1002/anie.201806792","title":"A Mechanism‐Based Approach to Screening Metagenomic Libraries for Discovery of Unconventional Glycosidases","year":2018,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Carbohydrate Chemistry and Synthesis","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada Council for the Arts","keywords":"Mechanism (biology); Metagenomics; Computational biology; Computer science; Data science; Drug discovery; Biochemical engineering; Biology; Bioinformatics; Engineering; Biochemistry; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001248621,0.0001743584,0.0002018707,0.00008633669,0.00009070185,0.00009400079,0.0002928238,0.0001107191,0.0008035993],"category_scores_gemma":[0.0002331137,0.0001811502,0.0002261798,0.00009894835,0.0001090365,0.0003915963,0.00006541251,0.00006929661,0.000005850683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007534333,"about_ca_system_score_gemma":0.00007306532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001743723,"about_ca_topic_score_gemma":0.000002193593,"domain_scores_codex":[0.9988337,0.000005862429,0.000323111,0.0003431397,0.0003179756,0.0001762551],"domain_scores_gemma":[0.9991017,0.0001923729,0.0001818321,0.0001940821,0.0002603677,0.00006964936],"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":[0.000383102,0.0001708123,0.00004067528,0.0001294631,0.0001567569,7.500277e-7,0.0000772581,0.00003681425,0.9914871,0.004221663,0.003076117,0.0002195037],"study_design_scores_gemma":[0.0005831493,0.00002725755,0.0000160243,0.000114145,0.00006004433,0.00000423244,0.0001690321,0.0009791079,0.9889835,0.003312221,0.005562263,0.0001890682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2905898,0.00005577026,0.6547027,0.0007147589,0.0007595484,0.0002323988,0.002859904,0.0001305578,0.04995448],"genre_scores_gemma":[0.9820031,0.000002631041,0.0119866,0.0001780703,0.002095531,0.0001818775,0.001992008,0.0000263536,0.001533843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6914132,"threshold_uncertainty_score":0.8798849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03115136134546231,"score_gpt":0.2552762830131166,"score_spread":0.2241249216676542,"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."}}