{"id":"W2950746887","doi":"10.1128/msystems.00053-18","title":"Balances: a New Perspective for Microbiome Analysis","year":2018,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":304,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Salud Carlos III; Ministerio de Economía y Competitividad; University of Victoria; European Regional Development Fund; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Microbiome; Identification (biology); Perspective (graphical); Computational biology; Computer science; Biology; Artificial intelligence; Bioinformatics; Ecology","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":[0.0001234449,0.0001015679,0.0001797905,0.00007372379,0.00008875717,0.00002437153,0.0001303002,0.0001033091,0.00002788654],"category_scores_gemma":[0.0000180459,0.00009099009,0.0001548722,0.0002227598,0.00004624326,0.000001285413,0.00002752135,0.00002542298,0.00004210885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003474752,"about_ca_system_score_gemma":0.0001150246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005032121,"about_ca_topic_score_gemma":0.0007767786,"domain_scores_codex":[0.999242,0.0000246808,0.0001479176,0.000317757,0.00003739254,0.0002302562],"domain_scores_gemma":[0.9994311,0.000005278231,0.00006863055,0.0002664607,0.0001437914,0.00008472718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005849979,0.00002227696,0.001957293,0.00002794896,0.0003893891,2.750641e-7,0.0002941602,0.000001935205,0.9553617,0.0004428315,0.04135295,0.00009076481],"study_design_scores_gemma":[0.00120539,0.000843131,0.004200849,0.00002477799,0.0002919553,0.00001860737,0.001031972,0.00009987667,0.1044558,0.00009177066,0.8873738,0.0003620847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9146137,0.004584514,0.06479118,0.001576608,0.001415594,0.001463025,0.0003644818,0.00007101354,0.01111992],"genre_scores_gemma":[0.9904075,0.00001753727,0.001073072,0.0003257901,0.001257603,0.00001563969,0.0001114373,0.00001389189,0.006777562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8509059,"threshold_uncertainty_score":0.3710468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306933650816789,"score_gpt":0.3106530368258283,"score_spread":0.2975837003176604,"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."}}