{"id":"W3094190624","doi":"10.1101/2020.10.20.347948","title":"Epistatic interactions shape the interplay between beneficial alleles and gain or loss of pathways in the evolution of novel metabolism","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institutes of Health; National Science Foundation","keywords":"Epistasis; Biology; Allele; Genetics; Context (archaeology); Metabolic pathway; Gene; Phenotype; Fitness landscape; Computational biology; Evolutionary biology; Population","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006679527,0.0003226877,0.0003943479,0.000622885,0.0004248395,0.001147943,0.0003471065,0.0005891184,0.003110126],"category_scores_gemma":[0.0016458,0.0002752408,0.00031094,0.0003582052,0.0005470539,0.0005067639,0.001353786,0.0009258275,0.0002292416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004877655,"about_ca_system_score_gemma":0.0003225226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007989145,"about_ca_topic_score_gemma":0.002135016,"domain_scores_codex":[0.9995467,0.0001419837,0.0000365963,0.0001040455,0.00009438522,0.00007633022],"domain_scores_gemma":[0.9992878,0.0003327482,0.0001336302,0.00005806191,0.00007605842,0.0001117637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003587942,0.000112335,0.0395789,0.0001431831,0.0001895094,0.000519358,0.0002074513,0.009647349,0.9245064,0.005638844,0.0005583889,0.01853937],"study_design_scores_gemma":[0.0001171482,0.001148465,0.5247714,0.00008544939,0.0007870282,0.003523847,0.002204972,0.2533829,0.1697825,0.03296465,0.01088271,0.000348962],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912289,0.0001733496,0.006391175,0.0001329148,0.000009599858,0.0000102094,0.0001216008,0.00007390195,0.001858257],"genre_scores_gemma":[0.9979823,0.00005919009,0.001540732,0.00005913942,0.000003000264,0.000007153096,0.00004599598,0.00001901005,0.0002834942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003110126,"threshold_uncertainty_score":0.01040447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870682615566141,"score_gpt":0.2593635818671395,"score_spread":0.2406567557114781,"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."}}