{"id":"W2110300022","doi":"10.1038/nbt.2676","title":"Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences","year":2013,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9357,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institutes of Health Research; National Institute of General Medical Sciences; Crohn's and Colitis Foundation; National Human Genome Research Institute; Howard Hughes Medical Institute","keywords":"Metagenomics; Biology; Phylogenetic tree; 16S ribosomal RNA; Gene; Computational biology; Phylogenetics; Microbiome; Genetics; Ribosomal RNA; Genome; Evolutionary 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":[0.0009225721,0.0006815416,0.0005960618,0.001184854,0.0002978914,0.0008479313,0.0003104992,0.0005167661,0.0004414275],"category_scores_gemma":[0.001297665,0.0002381025,0.0005929357,0.0009910781,0.0003218493,0.0005961526,0.0004248052,0.0007347902,0.0003828961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002606033,"about_ca_system_score_gemma":0.0003369077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008508855,"about_ca_topic_score_gemma":0.001104616,"domain_scores_codex":[0.9995139,0.0001025688,0.00002406598,0.0001515944,0.0001427663,0.000065215],"domain_scores_gemma":[0.9993781,0.0002421764,0.0001532436,0.00004842705,0.0001214172,0.00005667628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004590342,0.000246344,0.02160641,0.0001261906,0.00004530617,0.00004639863,0.00006805836,0.002253212,0.9493935,0.0002295076,0.0001036201,0.02542247],"study_design_scores_gemma":[0.00005270046,0.001708561,0.1589675,0.00007035919,0.0002208182,0.0005010305,0.0004005726,0.1031982,0.7306535,0.002273815,0.001885534,0.0000673974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548543,0.001147105,0.03961011,0.0001518582,0.00003101025,0.0001035471,0.002750039,0.0002396368,0.001112403],"genre_scores_gemma":[0.9630158,0.0008505268,0.03260259,0.00007868927,0.00002459004,0.0000947309,0.002713594,0.00002765777,0.0005919149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001184854,"threshold_uncertainty_score":0.004879117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096311623802304,"score_gpt":0.2455102926772277,"score_spread":0.2345471764392046,"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."}}