{"id":"W2461711360","doi":"10.1186/s40168-016-0176-z","title":"MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota","year":2016,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Government of Canada; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Ontario Genomics Institute","keywords":"Metaproteomics; Biology; Metagenomics; Gut flora; Computational biology; Lipidome; Microbiology; Bioinformatics; Genetics; Gene; Immunology; Lipidomics","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":[0.0002796619,0.0003178167,0.000323561,0.0001949285,0.0002364498,0.00005586696,0.0003422551,0.0002042507,0.00002883658],"category_scores_gemma":[0.00001525801,0.0002439089,0.0001102668,0.0001757724,0.0001544299,0.00001021877,0.0004034212,0.00009535161,0.00007521087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008117359,"about_ca_system_score_gemma":0.0000868288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008511353,"about_ca_topic_score_gemma":0.00003892478,"domain_scores_codex":[0.9981847,0.00009667416,0.0002945887,0.0008016224,0.00006478962,0.0005575658],"domain_scores_gemma":[0.9990613,0.000006852347,0.00009445721,0.0005462588,0.00007452187,0.0002165787],"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":[0.00005240203,0.0001264746,0.0002901381,0.00004256018,0.0001111953,0.000001863879,0.0001437083,5.17485e-7,0.9930442,0.0001011477,0.005627423,0.0004583977],"study_design_scores_gemma":[0.001969195,0.0003531897,0.00290166,0.00002958145,0.0000668943,0.0000921732,0.000161631,5.610278e-7,0.9093826,0.00001017882,0.08444855,0.0005838015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956912,0.000361568,0.002018694,0.0004432382,0.00007674631,0.0006913217,0.0001629984,0.00003906709,0.0005151269],"genre_scores_gemma":[0.9826472,0.00005067097,0.007031533,0.0007194522,0.0001271821,0.00002654025,0.0001014175,0.00006221449,0.009233772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08366159,"threshold_uncertainty_score":0.9946318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698193715608017,"score_gpt":0.2470030957009087,"score_spread":0.2300211585448285,"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."}}