{"id":"W3036145340","doi":"10.2139/ssrn.3318927","title":"The Landscape of Genetic Content in the Human Microbiome","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Microbiome; Content (measure theory); Human microbiome; Metagenomics; Biology; Computational biology; Genetics; Evolutionary biology; Geography; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002864133,0.0001220973,0.0003851826,0.0009810256,0.0004004398,0.001789511,0.0001804484,0.000564517,0.002796188],"category_scores_gemma":[0.001441821,0.0001518694,0.0001407636,0.001116956,0.0008070017,0.0007685683,0.0008171534,0.0004418635,0.0003525001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559242,"about_ca_system_score_gemma":0.0002460374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009931655,"about_ca_topic_score_gemma":0.001374826,"domain_scores_codex":[0.9996145,0.0001333659,0.00001341144,0.0001142003,0.00007150771,0.00005300417],"domain_scores_gemma":[0.9995691,0.0001623798,0.0001012033,0.00003876365,0.00005510907,0.00007352169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00102165,0.0001498423,0.5064355,0.0004464903,0.000598689,0.0007396052,0.001752537,0.003632879,0.2702224,0.03002774,0.00334439,0.1816283],"study_design_scores_gemma":[0.00002126901,0.000311243,0.9277494,0.0001166114,0.000142287,0.001371674,0.001764841,0.004221663,0.006019385,0.0446679,0.01354789,0.00006597286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799941,0.007323835,0.003146192,0.002320964,0.00004927921,0.000006676871,0.0009765201,0.00005602741,0.006126341],"genre_scores_gemma":[0.9963923,0.001508039,0.0008919059,0.000286775,0.00006194236,0.000004600294,0.0001596608,0.00001797973,0.0006767497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002796188,"threshold_uncertainty_score":0.009354234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007930499374027032,"score_gpt":0.2422488973815456,"score_spread":0.2343183980075185,"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."}}