{"id":"W2896843491","doi":"10.1007/978-1-4939-8728-3_17","title":"Bayesian Inference of Microbial Community Structure from Metagenomic Data Using BioMiCo","year":2018,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Metagenomics; Computer science; Community structure; Inference; Data mining; Sample (material); Bayesian probability; Bayesian inference; Set (abstract data type); Prior probability; Machine learning; Artificial intelligence; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006791402,0.000966804,0.001617072,0.001892307,0.001087145,0.002167107,0.00210871,0.001624796,0.001100636],"category_scores_gemma":[0.02535325,0.00177454,0.002071885,0.001188395,0.001179431,0.002491798,0.002322505,0.003455351,0.0006440381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009113737,"about_ca_system_score_gemma":0.001666987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006137664,"about_ca_topic_score_gemma":0.00967498,"domain_scores_codex":[0.997771,0.001274252,0.0001001332,0.0004880927,0.0002728171,0.00009369355],"domain_scores_gemma":[0.9882287,0.009314863,0.0005374374,0.001081467,0.000547077,0.0002903923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00110965,0.000332507,0.02438148,0.000538863,0.00141572,0.0001922302,0.0004484816,0.7762207,0.01737067,0.03891227,0.002019689,0.1370578],"study_design_scores_gemma":[0.00004857463,0.00002996529,0.001785769,0.00002778823,0.00005029534,0.00003130041,0.00002317652,0.9550809,0.001288409,0.04101847,0.0005869524,0.00002840974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05250208,0.0005102104,0.9451312,0.0002287707,0.00003578559,0.00004684792,0.0005105426,0.0006104917,0.0004240694],"genre_scores_gemma":[0.4862337,0.0006750412,0.508312,0.000285879,0.0001096133,0.000273861,0.002728013,0.0003600893,0.00102176],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006791402,"threshold_uncertainty_score":0.03591675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07280853400990732,"score_gpt":0.4491861699346388,"score_spread":0.3763776359247315,"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."}}