{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002030471,0.0004711849,0.0004575695,0.001069494,0.0002567405,0.0005630383,0.000767023,0.0005509537,0.001059451],"category_scores_gemma":[0.001991859,0.0001395354,0.0006296244,0.0007228319,0.0006675555,0.0002596539,0.0004382594,0.000740435,0.0000769649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247861,"about_ca_system_score_gemma":0.0003332422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001957177,"about_ca_topic_score_gemma":0.001290615,"domain_scores_codex":[0.9992693,0.0003718119,0.00002433131,0.0001701384,0.000118758,0.0000456337],"domain_scores_gemma":[0.9989702,0.0008081319,0.00008752697,0.00005083162,0.00004221265,0.00004119509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006276765,0.00037405,0.02298731,0.0001677574,0.0004256369,0.0001657559,0.0002130017,0.07803512,0.8605655,0.01164118,0.0003921985,0.02440488],"study_design_scores_gemma":[0.00005994628,0.000666227,0.0726806,0.00001593212,0.0001973363,0.0002323372,0.0001868802,0.8268423,0.09257913,0.005198628,0.001241988,0.00009872345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521723,0.00009464919,0.04627562,0.00008553028,0.000009690238,0.00002595288,0.0004803679,0.0001673005,0.0006885249],"genre_scores_gemma":[0.9724188,0.00006134377,0.02647447,0.00006394256,0.000005834395,0.00007674205,0.0004500416,0.000063093,0.0003857449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002030471,"threshold_uncertainty_score":0.01073831,"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."}}