{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001271255,0.0001117555,0.0001246453,0.0001817391,0.0001046956,0.00006701918,0.00008441402,0.0001207925,0.000005488623],"category_scores_gemma":[0.00007516857,0.0001132386,0.00004277421,0.00025499,0.0001361353,0.00001059134,0.00006835637,0.0000675196,0.00001132051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008630647,"about_ca_system_score_gemma":0.0003816451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004151696,"about_ca_topic_score_gemma":0.0001427026,"domain_scores_codex":[0.9984472,0.00005462564,0.0003886579,0.0006989775,0.000129352,0.0002812029],"domain_scores_gemma":[0.9988359,0.000001841422,0.0002210094,0.000598849,0.0002152449,0.0001271197],"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":[0.00001021058,0.00004614755,0.002897162,0.00001520501,0.000004250885,0.00001612746,0.0001498455,0.00004966569,0.9910143,0.00001059449,0.001037228,0.004749272],"study_design_scores_gemma":[0.0004947874,0.0001018304,0.01597946,0.00006255815,0.00001360381,0.003186773,0.0005420449,0.00007550392,0.8715751,0.001046597,0.1065266,0.000395167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907256,0.003872618,0.0001505393,0.0001368929,0.004680785,0.0002224459,0.000002695168,0.00001281521,0.0001955832],"genre_scores_gemma":[0.9968832,0.0001633393,0.002347334,0.00003296473,0.0001914328,0.000007024257,0.0001362869,0.00001281158,0.0002255894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1194392,"threshold_uncertainty_score":0.4617738,"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."}}