{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011831,0.0007149868,0.0009576182,0.001100424,0.0003700416,0.001074465,0.0005626919,0.0005328404,0.0006029776],"category_scores_gemma":[0.001661421,0.0003069417,0.0005611869,0.001518077,0.0002831727,0.0004259167,0.0008680769,0.001011242,0.0009003261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003335397,"about_ca_system_score_gemma":0.0005750108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001256473,"about_ca_topic_score_gemma":0.002272258,"domain_scores_codex":[0.9992777,0.0001114031,0.0001040424,0.0001398495,0.0002724646,0.00009471513],"domain_scores_gemma":[0.9992648,0.0002241407,0.0001062006,0.0001839274,0.0001427539,0.00007823736],"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":[0.00005355675,0.00006442532,0.001152533,0.00004648332,0.00001585939,0.00005882996,0.00005240125,0.0002043718,0.9925167,0.00009537212,0.00007988209,0.005659602],"study_design_scores_gemma":[0.00002643882,0.0003121769,0.01592896,0.00002179875,0.0000817311,0.000481835,0.0001530055,0.003652361,0.9742348,0.0002391607,0.004837457,0.00003010673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8759732,0.00120324,0.1059411,0.0004585255,0.00007992745,0.0009885279,0.01209521,0.0008333581,0.00242682],"genre_scores_gemma":[0.7962818,0.002080794,0.1593217,0.0002840173,0.00003920712,0.0009716213,0.03553972,0.0006726537,0.004808607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001256473,"threshold_uncertainty_score":0.005351126,"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."}}