{"id":"W2246105631","doi":"10.1038/srep16498","title":"Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; Centre for Chronic Diseases and Disorders; FP7 People: Marie-Curie Actions; National Institutes of Health; Fondation Maison des Sciences de l’Homme; Banco Bilbao Vizcaya Argentaria; Generalitat Valenciana; European Commission; Wellcome Trust; Fundación BBVA","keywords":"Metagenomics; Amplicon; Hypervariable region; Microbiome; Biology; Shotgun sequencing; Computational biology; 16S ribosomal RNA; Genetics; Human Microbiome Project; In silico; Shotgun; Deep sequencing; Ribosomal RNA; Evolutionary biology; DNA sequencing; Gene; Polymerase chain reaction; Genome","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":[0.00775851,0.0007045966,0.0008368145,0.001253258,0.0009067697,0.002292544,0.001027008,0.0009576529,0.0004254169],"category_scores_gemma":[0.01446955,0.0005248867,0.0004537074,0.001318763,0.001466222,0.001312673,0.001962498,0.001145195,0.0005946337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003436042,"about_ca_system_score_gemma":0.0007021081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006532211,"about_ca_topic_score_gemma":0.00155483,"domain_scores_codex":[0.9951905,0.00194303,0.0004106396,0.0008003995,0.001461585,0.0001939145],"domain_scores_gemma":[0.9943768,0.002854787,0.0009420739,0.0007491256,0.0008912132,0.0001860332],"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":[0.0003469024,0.0001120664,0.0960847,0.0009074116,0.0002038642,0.0008615017,0.002108972,0.00934579,0.7656553,0.00369265,0.0002253109,0.1204554],"study_design_scores_gemma":[0.00002788865,0.0008571413,0.1943568,0.0006289232,0.0002652067,0.005580254,0.004099288,0.08255781,0.6651873,0.03078207,0.01548908,0.0001682372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7663548,0.003519628,0.2273225,0.0004944616,0.000064279,0.00008985298,0.000357708,0.0002869556,0.001509746],"genre_scores_gemma":[0.7809214,0.002429535,0.2151536,0.0002502415,0.00003307953,0.0001076911,0.0004691557,0.000108075,0.000527148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00775851,"threshold_uncertainty_score":0.04103142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07462589348378279,"score_gpt":0.2930344357708931,"score_spread":0.2184085422871103,"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."}}