{"id":"W3038111896","doi":"10.1074/mcp.r120.002051","title":"Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface","year":2020,"lang":"en","type":"review","venue":"Molecular & Cellular Proteomics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"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; Genome Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Ontario Genomics Institute","keywords":"Metaproteomics; Proteomics; Microbiome; Metagenomics; Computational biology; Computer science; Energy flow; Ecosystem; Ecology; Biology; Biochemical engineering; Bioinformatics; Energy (signal processing); Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008235588,0.0009745828,0.001285852,0.0001247832,0.000739622,0.0002401958,0.0007052545,0.0006793797,0.00001009491],"category_scores_gemma":[0.0001069716,0.000659781,0.0005094512,0.0002727978,0.0002080368,0.000009953847,0.001907257,0.0007927925,0.00006863402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000219537,"about_ca_system_score_gemma":0.0005866478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004636918,"about_ca_topic_score_gemma":0.00009669205,"domain_scores_codex":[0.9959613,0.0006033842,0.0009893333,0.001492202,0.000248846,0.0007049167],"domain_scores_gemma":[0.997682,0.00004210231,0.0005095921,0.001247972,0.0001283342,0.0003900385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002146847,0.00007290286,0.000004986846,0.006252703,0.00102631,0.0000550481,0.0001426819,0.0004840816,0.9768855,0.0002232212,0.001878159,0.01275972],"study_design_scores_gemma":[0.0005207183,0.0003438294,7.405539e-7,0.001031359,0.001096228,0.0006709903,0.00008237853,0.002249185,0.10838,0.00003793105,0.8845217,0.001064949],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1122138,0.837392,0.04132887,0.0007572015,0.0003023437,0.007635747,0.0003162763,0.00003242264,0.00002129712],"genre_scores_gemma":[0.01113474,0.9758638,0.008419287,0.000674204,0.0006048941,0.001920728,0.0006839932,0.0003650253,0.0003332899],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8826435,"threshold_uncertainty_score":0.9995853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187323177922467,"score_gpt":0.2689781381810543,"score_spread":0.2371049064018297,"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."}}