{"id":"W2340748026","doi":"10.3389/fmicb.2016.00459","title":"Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics","year":2016,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":941,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Innovates; Alberta Innovates - Technology Futures; Alberta Health Services","keywords":"Metagenomics; Microbiome; Computational biology; Shotgun sequencing; Biology; Shotgun; Amplicon sequencing; Amplicon; Human Microbiome Project; 16S ribosomal RNA; Ribosomal RNA; Data science; Human microbiome; DNA sequencing; Bioinformatics; Genetics; Computer science; Gene; Polymerase chain reaction","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":[0.002328023,0.0008945368,0.001199133,0.004173489,0.0005269839,0.001627765,0.0005127894,0.0007747414,0.0007109247],"category_scores_gemma":[0.003532879,0.0002567373,0.00146665,0.003194344,0.0005347487,0.001754842,0.0007895266,0.0006195974,0.0006137968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003742943,"about_ca_system_score_gemma":0.001013958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449252,"about_ca_topic_score_gemma":0.00226067,"domain_scores_codex":[0.9987444,0.000369531,0.0001753441,0.0002371887,0.0004012284,0.00007228358],"domain_scores_gemma":[0.9988255,0.0004380243,0.0002943229,0.0001338124,0.0002388191,0.00006941659],"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":[0.0002862923,0.000121017,0.02393742,0.005827122,0.0003294392,0.0006045514,0.0004034107,0.003259319,0.7776603,0.002067434,0.0005813332,0.1849223],"study_design_scores_gemma":[0.00004197473,0.001644963,0.1315566,0.002127946,0.0009646659,0.002770083,0.002241262,0.02133084,0.7654983,0.01694011,0.05457325,0.0003100773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4875986,0.1117208,0.3703323,0.002290539,0.0005430673,0.0009436218,0.01420203,0.00101586,0.01135319],"genre_scores_gemma":[0.4275287,0.08199695,0.4798533,0.0005781139,0.0002098706,0.0006159192,0.006962753,0.0001626959,0.002091665],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004173489,"threshold_uncertainty_score":0.01231194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200830776422221,"score_gpt":0.2366186626662136,"score_spread":0.2246103549019914,"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."}}