{"id":"W2948476002","doi":"10.1128/msystems.00082-19","title":"High-Throughput Recovery and Characterization of Metagenome-Derived Glycoside Hydrolase-Containing Clones as a Resource for Biocatalyst Development","year":2019,"lang":"en","type":"article","venue":"mSystems","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Genome British Columbia; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Genome Canada","keywords":"Fosmid; Metagenomics; Glycoside hydrolase; Biology; Context (archaeology); Computational biology; High-throughput screening; Directed evolution; Biochemistry; Cellulase; Gene; Enzyme","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":[0.0002276953,0.0001249792,0.0002049326,0.00005556516,0.00005836208,0.00002573119,0.00007001484,0.0001014512,0.000008194251],"category_scores_gemma":[0.00003412286,0.0001234377,0.00004092744,0.00006550627,0.0000190896,0.00001145174,0.00004727427,0.00002872443,0.00001000232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001487614,"about_ca_system_score_gemma":0.00004517818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001610677,"about_ca_topic_score_gemma":0.000004637035,"domain_scores_codex":[0.9990941,0.00004581641,0.0003154535,0.0003295385,0.000085162,0.0001299078],"domain_scores_gemma":[0.999425,0.000009489822,0.0002381213,0.0002146962,0.00007482404,0.00003787679],"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":[0.000231382,0.0000205732,0.001188395,0.0001380266,0.00007578837,1.815081e-7,0.0001910541,0.00001368222,0.9969189,0.00004642354,0.00001861674,0.001156933],"study_design_scores_gemma":[0.0005620171,0.000249374,0.002921878,0.00004482614,0.00001802069,0.00001052198,0.0001312089,0.0000358711,0.9320982,0.000004124626,0.06377406,0.0001499233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957585,0.0001887203,0.003073054,0.00008804622,0.0002810851,0.000501622,0.00002638477,0.00001573613,0.00006680525],"genre_scores_gemma":[0.9961972,0.00004796801,0.0004595368,0.0001104011,0.0001835233,0.0000646155,0.001566207,0.00002528694,0.001345318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06482077,"threshold_uncertainty_score":0.5033641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007518379794887824,"score_gpt":0.2088198489311232,"score_spread":0.2013014691362354,"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."}}