{"id":"W2922092653","doi":"10.1093/bioinformatics/btz175","title":"SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Population; Evolutionary dynamics; Fitness landscape; Biology; Context (archaeology); Experimental evolution; Selection (genetic algorithm); Population size; Fixation (population genetics); Protein dynamics; Evolutionary biology; Computer science; Protein structure; Genetics; Artificial intelligence; Gene","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":[0.001345608,0.001892376,0.001302289,0.0008178689,0.0007063413,0.00116695,0.004227973,0.001944092,0.01611168],"category_scores_gemma":[0.005930543,0.0008876337,0.001522238,0.0007641223,0.0006979386,0.0009382028,0.001497462,0.002566104,0.002148145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001025939,"about_ca_system_score_gemma":0.002540803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008985845,"about_ca_topic_score_gemma":0.01126364,"domain_scores_codex":[0.9996449,0.0001332589,0.00002885634,0.00004599749,0.00009560807,0.00005139188],"domain_scores_gemma":[0.9979643,0.00146423,0.00008455165,0.0001013686,0.0002263375,0.0001591761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004918159,0.0003735947,0.008153859,0.001854828,0.0005736371,0.000819928,0.0004116092,0.8585315,0.005088513,0.02882718,0.06422458,0.03064885],"study_design_scores_gemma":[0.0002886574,0.0000489126,0.0005533721,0.00004911757,0.00003320777,0.00006205533,0.00003232543,0.9776576,0.001011966,0.005826685,0.01440843,0.0000277236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2527453,0.002277648,0.5239723,0.002556941,0.00120988,0.001700262,0.04897075,0.1185007,0.04806628],"genre_scores_gemma":[0.4462227,0.002227785,0.4563713,0.001305756,0.0002002841,0.005634809,0.05674309,0.01890132,0.01239285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01611168,"threshold_uncertainty_score":0.05389893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345584259753868,"score_gpt":0.2716877545153,"score_spread":0.2582319119177613,"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."}}