{"id":"W2763563782","doi":"10.1101/205542","title":"The response to selection in Glycoside Hydrolase Family 13 structures: A comparative quantitative genetics approach","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre; McGill Genome Centre; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Glycoside hydrolase; Selection (genetic algorithm); Hydrolysis; Phylogenetic tree; Stability (learning theory); Computational biology; Function (biology); Biology; Rank (graph theory); Hydrolase; Genetics; Evolutionary biology; Biochemistry; Computer science; Enzyme; Mathematics; Machine learning; Gene; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007924115,0.0004250442,0.0003426506,0.0001664186,0.0004000362,0.0003045891,0.000579253,0.0004370773,0.000001576944],"category_scores_gemma":[0.0004827431,0.0003932534,0.00009511344,0.0002288037,0.0001464721,0.00001245465,0.0003787578,0.0005035384,0.00001363467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001265467,"about_ca_system_score_gemma":0.0005097846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003397424,"about_ca_topic_score_gemma":0.00003028667,"domain_scores_codex":[0.997452,0.0004910661,0.0004249487,0.0009914555,0.0002515469,0.0003889746],"domain_scores_gemma":[0.9979599,0.0000355936,0.0004149218,0.0009714583,0.00046077,0.0001573399],"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":[0.001101009,0.00006980231,0.00116125,0.00003359852,0.00008661432,0.000002610852,0.00006235326,0.001966557,0.9948927,0.0001009887,0.0005207439,0.000001747093],"study_design_scores_gemma":[0.0005532813,0.000239063,0.1730348,0.00005796243,0.00003766414,6.017452e-8,0.0000415274,0.001449362,0.8063133,0.000004587211,0.01765171,0.0006166691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916859,0.001311341,0.005100757,0.0002616839,0.0005500788,0.0009217364,0.0001075466,0.00004771843,0.00001322756],"genre_scores_gemma":[0.9941314,0.0004275153,0.004524027,0.0001898778,0.0003446121,0.0002838421,0.000003479046,0.00006584927,0.00002933286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1885795,"threshold_uncertainty_score":0.9998519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616152702155253,"score_gpt":0.2739614790951976,"score_spread":0.2477999520736451,"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."}}