{"id":"W2912883243","doi":"10.1038/s41587-018-0009-7","title":"A human gut bacterial genome and culture collection for improved metagenomic analyses","year":2019,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":630,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Biotechnology and Biological Sciences Research Council; European Molecular Biology Laboratory; Medical Research Council; National Health and Medical Research Council; State Government of Victoria; European Bioinformatics Institute; Wellcome Trust","keywords":"Metagenomics; Biology; Microbiome; Genome; Shotgun sequencing; Bacterial genome size; Human Microbiome Project; Computational biology; Shotgun; Subspecies; Human microbiome; Human gastrointestinal tract; Bacteria; Genetics; Gene; Zoology","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00009225244,0.0001625744,0.00022964,0.0001131883,0.000126964,0.0000196145,0.0001543692,0.00169969,0.0000249473],"category_scores_gemma":[0.00003050496,0.0001418051,0.00008164637,0.0001117847,0.00006877357,0.000002859614,0.00009474498,0.0003114161,0.00000502089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002838077,"about_ca_system_score_gemma":0.00005338297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001429614,"about_ca_topic_score_gemma":0.00009835499,"domain_scores_codex":[0.9990752,0.0000216505,0.0001576856,0.0004653277,0.00002525775,0.0002549202],"domain_scores_gemma":[0.9994994,0.000003560382,0.00009577817,0.0003030983,0.00005838858,0.00003976799],"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":[0.00009335196,0.00002053688,0.00004381937,0.00003176504,0.00008148129,2.214583e-7,0.00001639523,3.947433e-7,0.9971279,0.0002547627,0.002117243,0.0002121345],"study_design_scores_gemma":[0.001295933,0.00098827,0.002455322,0.000003803896,0.00005127956,0.00003294353,0.00003753384,0.00001244305,0.6066236,0.0001634244,0.3881177,0.0002177786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949933,0.002506759,0.0001961098,0.001118273,0.0003251004,0.0006452836,0.00008302716,0.0000410364,0.00009107836],"genre_scores_gemma":[0.9935994,0.0002944189,0.002672412,0.0004284817,0.0002352149,0.00002364855,0.0003973212,0.00002332252,0.002325841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3905043,"threshold_uncertainty_score":0.9995963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00906531627041473,"score_gpt":0.3040566969396096,"score_spread":0.2949913806691949,"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."}}