{"id":"W2140580533","doi":"10.1186/s40168-015-0073-x","title":"BioMiCo: a supervised Bayesian model for inference of microbial community structure","year":2015,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Universities Space Research Association; Canadian Institutes of Health Research; Tula Foundation","keywords":"Microbiome; Prior probability; Inference; Dirichlet distribution; Bayesian probability; Bayesian inference; Community structure; Biology; Artificial intelligence; Abundance (ecology); Machine learning; Sample (material); Metagenomics; Human microbiome; Relative species abundance; Ecology; Computer science; Mathematics; Bioinformatics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002403057,0.0001962974,0.0002562308,0.0000856285,0.0001097241,0.00001965246,0.0003881137,0.0002596992,0.000009745384],"category_scores_gemma":[0.00005202664,0.000192001,0.0001106979,0.0001138774,0.000143963,0.000005066172,0.0001717712,0.0001411375,0.00000324673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003405157,"about_ca_system_score_gemma":0.0003758983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001825656,"about_ca_topic_score_gemma":0.0003526225,"domain_scores_codex":[0.9990011,0.00008850153,0.0003008257,0.0002397394,0.00004515913,0.0003246529],"domain_scores_gemma":[0.9990128,0.00001292705,0.000110874,0.0005026573,0.0002253409,0.0001354197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001730015,0.0001035653,0.0002715788,0.0001131767,0.00003130331,1.643119e-7,0.0005697918,0.00008020402,0.9910306,0.00003804431,0.007333474,0.0002551116],"study_design_scores_gemma":[0.002769179,0.0004682472,0.0003022103,0.00003332578,0.00003128035,0.0000237216,0.0001847808,0.001946992,0.9856734,0.0003694246,0.00784152,0.000355953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768768,0.0001812484,0.02086305,0.0001307567,0.0001381651,0.0003763306,0.001367761,0.00001284165,0.00005305026],"genre_scores_gemma":[0.9862061,0.000009435111,0.01176138,0.0004008366,0.00006984253,0.000007237523,0.001198675,0.00003019657,0.0003162712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009329329,"threshold_uncertainty_score":0.7829576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03994741837461974,"score_gpt":0.2998954274602382,"score_spread":0.2599480090856185,"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."}}