{"id":"W3197340937","doi":"10.1016/j.tibs.2021.08.004","title":"A BOSSS method for managing insights into diet–microbiome interactions","year":2021,"lang":"en","type":"article","venue":"Trends in Biochemical Sciences","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institutes of Health; National Institute of General Medical Sciences; Canadian Institute for Advanced Research","keywords":"Gut microbiome; Microbiome; Bioorthogonal chemistry; Computational biology; Metagenomics; Identification (biology); Biology; Microbial metabolism; Host (biology); Biochemistry; Bioinformatics; Bacteria; Chemistry; Ecology; Genetics; Combinatorial chemistry; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225483,0.0001328027,0.0001782954,0.000163069,0.0001616923,0.00004681449,0.0002303374,0.00006688367,0.00003955124],"category_scores_gemma":[0.0001313149,0.0001128899,0.0001116503,0.0007372237,0.0001745749,0.00000686936,0.0002315029,0.00007322762,0.000001920377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002476552,"about_ca_system_score_gemma":0.0000404831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003220955,"about_ca_topic_score_gemma":0.000203084,"domain_scores_codex":[0.9988445,0.00003852741,0.0002042208,0.0005433506,0.00009121408,0.0002781786],"domain_scores_gemma":[0.9996123,0.00004587909,0.0000536949,0.0001793407,0.00005295192,0.00005588933],"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":[0.00002949584,0.00006483232,0.0002008801,0.0000092091,0.00002637153,0.0000015802,0.0000790339,0.000006712388,0.9889333,0.001753565,0.002683388,0.00621168],"study_design_scores_gemma":[0.0003257527,0.0001415584,0.0004757281,0.00001336969,0.00001452188,0.000007900265,0.0002569137,0.0003390175,0.9146193,0.003842306,0.07975804,0.0002056247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9652796,0.007334074,0.01229566,0.003409214,0.0005731112,0.0001121253,0.00001747482,0.00002175557,0.01095696],"genre_scores_gemma":[0.9306632,0.0003860018,0.06700895,0.000314617,0.0001688377,0.00004579765,0.00007952332,0.000008980214,0.001324037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07707465,"threshold_uncertainty_score":0.4603519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508995028566527,"score_gpt":0.3597912009771543,"score_spread":0.3347012506914891,"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."}}