{"id":"W4311575706","doi":"10.1093/bioinformatics/btac809","title":"Efficient computation of contributional diversity metrics from microbiome data with <i>FuncDiv</i>","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Dalhousie University; McGill Genome Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"License; Metagenomics; Computer science; Microbiome; ENCODE; Diversity (politics); Function (biology); Alpha diversity; Data mining; Computational biology; Data science; Biology; Bioinformatics; Biodiversity; Genetics; Gene; Ecology","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":[0.0002326132,0.00008489312,0.0001218444,0.00006056722,0.0002817491,0.000009429295,0.0003392275,0.00004059118,0.00002198286],"category_scores_gemma":[0.0000202294,0.00007868197,0.00002772137,0.0002001051,0.00005814707,0.000003611335,0.001394764,0.00007429013,0.000004864805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003680876,"about_ca_system_score_gemma":0.0001465799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008043398,"about_ca_topic_score_gemma":0.000009850681,"domain_scores_codex":[0.9992669,0.00002906818,0.0002295333,0.0001359531,0.000193392,0.0001451458],"domain_scores_gemma":[0.9993293,0.00001619432,0.0001980417,0.0003139312,0.00009675432,0.00004579704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002303435,0.002469555,0.07095859,0.0007494112,0.001246833,0.00001632576,0.004905015,0.06057724,0.6845369,0.0008178797,0.1633639,0.008054879],"study_design_scores_gemma":[0.0253239,0.006275467,0.1235897,0.0001522546,0.0008745473,0.0003080012,0.008526997,0.4363188,0.08322503,0.0002126192,0.3121026,0.003090035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546444,0.0002976967,0.03806938,0.00008375344,0.0001448415,0.0002094506,0.006408545,0.000009622361,0.0001323152],"genre_scores_gemma":[0.9806954,0.00001256098,0.01039011,0.0003063339,0.00002630343,0.000001621337,0.008544344,0.000005311006,0.00001804095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6013119,"threshold_uncertainty_score":0.3208557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919680741520582,"score_gpt":0.2424538403965927,"score_spread":0.2232570329813868,"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."}}