{"id":"W3180790175","doi":"10.21203/rs.3.rs-785043/v1","title":"Revealing Protein-Level Functional Redundancy in the Human Gut Microbiome using Ultra-deep Metaproteomics","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Ministero dello Sviluppo Economico; National Institutes of Health; Government of Canada; Genome Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Compute Canada; Ontario Genomics Institute; Western Canada Research Grid; University of Ottawa","keywords":"Metaproteomics; Computational biology; Gut microbiome; Microbiome; Redundancy (engineering); Metagenomics; Computer science; Biology; Artificial intelligence; Bioinformatics; Biochemistry; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000726157,0.0006519224,0.0007908541,0.0007357186,0.0003053111,0.0009094194,0.0003859847,0.0006010755,0.0009319102],"category_scores_gemma":[0.001137311,0.0004571455,0.0008043162,0.0007588743,0.0003357132,0.0007648366,0.001140002,0.000902521,0.0003662544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002134968,"about_ca_system_score_gemma":0.0003739104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000896997,"about_ca_topic_score_gemma":0.001399909,"domain_scores_codex":[0.9997303,0.00005972634,0.00001048447,0.00009802561,0.00005369383,0.00004774549],"domain_scores_gemma":[0.9997043,0.000111413,0.00004397622,0.00006569632,0.00002911539,0.00004552105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001242048,0.0001893439,0.06419916,0.001197345,0.001824851,0.0003216505,0.0004115118,0.0185221,0.8134431,0.00388185,0.003134652,0.09163229],"study_design_scores_gemma":[0.0001226316,0.000509242,0.4057963,0.0001992369,0.001434053,0.001223371,0.0007162176,0.3681516,0.1487944,0.0604323,0.01242904,0.0001914826],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9396283,0.005839792,0.04505469,0.0009036596,0.00009157047,0.00001721394,0.006863471,0.0004855317,0.001115698],"genre_scores_gemma":[0.9714081,0.001717452,0.02103407,0.0002557347,0.00004352881,0.00002197427,0.00492003,0.000107666,0.0004914122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009319102,"threshold_uncertainty_score":0.003840387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1598839820707338,"score_gpt":0.4155097884975884,"score_spread":0.2556258064268546,"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."}}