{"id":"W2795353351","doi":"10.1128/msystems.00195-17","title":"Developing a <i>Bacteroides</i> System for Function-Based Screening of DNA from the Human Gut Microbiome","year":2018,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Government of Canada","keywords":"Bacteroides thetaiotaomicron; Metagenomics; Computational biology; Microbiome; Function (biology); Biology; Gut microbiome; Human Microbiome Project; Gene; DNA sequencing; Human microbiome; Annotation; Bacteroides fragilis; Bacteroides; DNA; Genetics; Bacteria","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003245192,0.0001408318,0.0002029725,0.0000302796,0.0002797714,0.00002994031,0.0002256011,0.0001384739,0.000004610367],"category_scores_gemma":[0.00001583656,0.0001075355,0.0001037704,0.00008257978,0.00007966195,0.000002143632,0.0000447286,0.0000443092,0.000009185483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003551563,"about_ca_system_score_gemma":0.0001288261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004506835,"about_ca_topic_score_gemma":0.0002756946,"domain_scores_codex":[0.9989728,0.00007749553,0.0003571868,0.0002868273,0.00006511372,0.0002406055],"domain_scores_gemma":[0.9991572,0.0000356031,0.0002125824,0.0003785857,0.0001751129,0.00004090411],"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":[0.00005756956,0.00000968743,0.002329348,0.0001908979,0.00006043776,1.529225e-7,0.00005133779,0.000001955333,0.9937442,0.0001446839,0.003284621,0.000125105],"study_design_scores_gemma":[0.001380531,0.000398895,0.007505083,0.0005547183,0.00005506087,0.00000760686,0.0004654943,0.00006572268,0.8078093,0.000008071088,0.181493,0.0002565657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9400263,0.000359884,0.05791337,0.0001752746,0.0005902474,0.0005552045,0.0002115087,0.00002457462,0.0001436103],"genre_scores_gemma":[0.996681,0.000001207583,0.001386125,0.0004446751,0.0008914454,0.00004014899,0.0003848305,0.00002681341,0.000143772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1859349,"threshold_uncertainty_score":0.4385169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600320082907076,"score_gpt":0.2683956389908012,"score_spread":0.2423924381617305,"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."}}