{"id":"W3087372435","doi":"10.1128/aem.01505-20","title":"High-Throughput Generation of Product Profiles for Arabinoxylan-Active Enzymes from Metagenomes","year":2020,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universiteit Gent; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Arabinoxylan; Biorefinery; Husk; Chemistry; Food science; Biomass (ecology); Enzymatic hydrolysis; Oligosaccharide; Pulp and paper industry; Biotechnology; Enzyme; Biochemical engineering; Biochemistry; Biology; Biofuel; Botany; Engineering; Agronomy","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":[0.0009902045,0.001106518,0.001216711,0.001107609,0.0003669146,0.001496315,0.0004656157,0.0007537338,0.0006473011],"category_scores_gemma":[0.001246992,0.0004453451,0.0009485187,0.001437427,0.0002401169,0.0007323756,0.0008999174,0.001333573,0.0008257472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003856685,"about_ca_system_score_gemma":0.0004160866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000735644,"about_ca_topic_score_gemma":0.001453418,"domain_scores_codex":[0.9992524,0.0001168956,0.0000752438,0.0002277195,0.0002416951,0.00008601024],"domain_scores_gemma":[0.9995868,0.000144677,0.00006036317,0.00005961448,0.00009893609,0.00004967002],"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":[0.0001977738,0.0001317615,0.0008983858,0.0002022173,0.00004843824,0.00005579633,0.00007545076,0.0005257914,0.9897766,0.00008518129,0.0001713435,0.007831258],"study_design_scores_gemma":[0.00007215042,0.0007904235,0.01653371,0.00006285376,0.0002062783,0.0002441662,0.0002913621,0.01250073,0.960314,0.0005734176,0.00831792,0.0000929535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8684714,0.003693008,0.07879544,0.0006693238,0.0001736837,0.0009957855,0.04227778,0.001423811,0.00349971],"genre_scores_gemma":[0.7938905,0.004160404,0.1282161,0.0003053738,0.00007462555,0.00108122,0.06773126,0.0006830848,0.003857386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001496315,"threshold_uncertainty_score":0.005236745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429455984698927,"score_gpt":0.1695883453962325,"score_spread":0.1552937855492433,"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."}}