{"id":"W2773939681","doi":"10.1038/nmeth.4458","title":"Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software","year":2017,"lang":"en","type":"article","venue":"Nature Methods","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":948,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Engineering and Physical Sciences Research Council; National Institute of Allergy and Infectious Diseases; Medical Research Council; Isaac Newton Institute for Mathematical Sciences; Deutsche Forschungsgemeinschaft; Biotechnology and Biological Sciences Research Council; Office of Science; Australian Research Council; Agency for Science, Technology and Research; Joint Genome Institute; U.S. Department of Energy; Division of Mathematical Sciences; Villum Fonden; H. Lundbeck A/S; Lundbeckfonden; National Science Foundation","keywords":"Metagenomics; Computational biology; Benchmark (surveying); Software; Computer science; Interpretation (philosophy); Biology; Genetics; Gene; Programming language; Geography; Cartography","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07267988,0.00230521,0.001260482,0.005226945,0.002125275,0.006419462,0.004411305,0.002049516,0.0021518],"category_scores_gemma":[0.160795,0.0009240197,0.001815662,0.004272817,0.002226992,0.004546349,0.006382083,0.003250683,0.001477533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003902433,"about_ca_system_score_gemma":0.006142096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002751025,"about_ca_topic_score_gemma":0.002258793,"domain_scores_codex":[0.949212,0.02305068,0.0041912,0.005005997,0.01693176,0.001608489],"domain_scores_gemma":[0.8615044,0.05506539,0.007732789,0.02616886,0.04562804,0.003900552],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004304365,0.001557319,0.08766099,0.003328982,0.001127781,0.0005265531,0.005335346,0.05533563,0.1118998,0.01688408,0.0596277,0.6524115],"study_design_scores_gemma":[0.0006849869,0.003213024,0.0465011,0.00168036,0.0004388246,0.0007836248,0.00258252,0.47572,0.3527698,0.03789442,0.07702255,0.0007088993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4610359,0.00519579,0.4410417,0.006980666,0.001147944,0.00191526,0.00776453,0.05519906,0.01971923],"genre_scores_gemma":[0.5827627,0.00076876,0.3881854,0.001332246,0.0001449475,0.001170537,0.01393425,0.01047162,0.001229471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9273201,"threshold_uncertainty_score":0.3843726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749655809789689,"score_gpt":0.4659042282284851,"score_spread":0.4484076701305882,"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."}}