{"id":"W4315631495","doi":"10.1126/sciadv.adc9130","title":"Machine learning enables prediction of metabolic system evolution in bacteria","year":2023,"lang":"en","type":"review","venue":"Science Advances","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Institute of Genetics; Japan Science and Technology Agency; Japan Society for the Promotion of Science","keywords":"Phylogenetic tree; Biology; Extant taxon; Predictability; Phylogenetic comparative methods; Metagenomics; Genome; Evolutionary biology; Adaptation (eye); Adaptive evolution; Bacterial genome size; Phylogenetics; Systems biology; Genome evolution; Gene; Molecular evolution; Computational biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005561645,0.0001626499,0.0005024236,0.0001934409,0.0001019606,0.00001378666,0.0002862449,0.00007990203,9.000774e-7],"category_scores_gemma":[0.0001162222,0.0001307038,0.00009770609,0.0005821191,0.0002162544,0.000003285807,0.0001664779,0.00007845255,0.000005086102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002939246,"about_ca_system_score_gemma":0.0002167291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003343903,"about_ca_topic_score_gemma":0.00003634459,"domain_scores_codex":[0.9987941,0.00006488837,0.0003670904,0.0004004241,0.000150957,0.0002225123],"domain_scores_gemma":[0.9994295,0.00001540315,0.0002665464,0.0001936358,0.00006366615,0.00003123917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001690272,0.00005089127,0.003266157,0.01664588,0.0001559452,0.000003235864,0.000117347,0.001122205,0.09997838,0.001374932,0.000009334789,0.8772588],"study_design_scores_gemma":[0.00009831894,0.0001023147,0.001072138,0.001762412,0.00008660856,0.000009521524,0.0001600466,0.00003899959,0.001154278,0.0000329497,0.9953004,0.0001820152],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002913369,0.9961247,0.00004099076,0.000001141529,0.0004193305,0.0002193491,0.00008474982,0.000005702746,0.0001906568],"genre_scores_gemma":[0.0109774,0.988467,0.0002117119,8.163086e-7,0.0001006314,0.00004877674,0.00004060302,0.00001346914,0.0001396017],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9952911,"threshold_uncertainty_score":0.5329945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231868189227067,"score_gpt":0.2920988669527342,"score_spread":0.2689120480300276,"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."}}