{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008261832,0.001033903,0.001370701,0.001542071,0.0001507161,0.001100821,0.001068372,0.001063837,0.001536836],"category_scores_gemma":[0.001323525,0.0004631401,0.0009136343,0.001707447,0.0004262635,0.001286372,0.0007067154,0.00162866,0.001145158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005652962,"about_ca_system_score_gemma":0.0008009135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001193328,"about_ca_topic_score_gemma":0.001022819,"domain_scores_codex":[0.9997655,0.00005703557,0.00001853149,0.00006563452,0.00007459137,0.00001884874],"domain_scores_gemma":[0.9994974,0.0003124056,0.00005541087,0.00001938771,0.00009110538,0.00002424686],"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":[0.0000721461,0.00005722612,0.0006576467,0.01058017,0.0003391226,0.0000958104,0.00002889194,0.006536313,0.001911661,0.007961773,0.008280191,0.9634791],"study_design_scores_gemma":[0.00006434941,0.0003871129,0.004527346,0.0063153,0.0008805731,0.001340282,0.00005608465,0.01853974,0.005769976,0.02856245,0.9334051,0.0001517298],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004552472,0.9955889,0.002697318,0.0002423817,0.0001188552,0.000006685887,0.00004139479,0.00003131898,0.0008179417],"genre_scores_gemma":[0.005196435,0.9915001,0.002240902,0.0001647118,0.0002271736,0.00001401039,0.0001065886,0.000009738653,0.0005402894],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001542071,"threshold_uncertainty_score":0.005141258,"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."}}