{"id":"W3036787676","doi":"10.1093/databa/baaa033","title":"FOBI: an ontology to represent food intake data and associate it with metabolomic data","year":2020,"lang":"en","type":"article","venue":"Database","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"European Institute of Innovation and Technology","keywords":"Ontology; Computer science; Open Biomedical Ontologies; Visualization; Data science; Ontology-based data integration; Information retrieval; Annotation; Workflow; Identification (biology); Data mining; Suggested Upper Merged Ontology; Database; Semantic Web; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003958357,0.0002099958,0.0003239022,0.00003795821,0.00009857445,0.00004687703,0.001162965,0.00006704699,0.00002860532],"category_scores_gemma":[0.0005814664,0.000176528,0.0000161013,0.0001445891,0.00007394573,0.00003126193,0.003966822,0.0001256717,0.00001171117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000520924,"about_ca_system_score_gemma":0.00006568592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277676,"about_ca_topic_score_gemma":0.002622424,"domain_scores_codex":[0.9979591,0.00009780636,0.0002230629,0.001258028,0.0001450288,0.0003169859],"domain_scores_gemma":[0.9969077,0.00002095842,0.0000950929,0.002644937,0.00004920435,0.0002821452],"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":[0.001744111,0.0004206711,0.01765397,0.0001093781,0.002011594,0.0000843948,0.0004546897,0.00001142862,0.594026,0.001502753,0.3737832,0.008197825],"study_design_scores_gemma":[0.001343922,0.001301829,0.003592505,0.000008857483,0.0002497841,0.00002894487,0.0005265138,0.0005754513,0.02042421,0.00002310118,0.971431,0.0004939516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500591,0.003417616,0.007366362,0.01131202,0.0001556699,0.0005159094,0.02666619,0.00003504166,0.0004720532],"genre_scores_gemma":[0.936976,0.001433447,0.02056111,0.006987911,0.00052095,0.00002372426,0.03335211,0.0000474687,0.00009728417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5976477,"threshold_uncertainty_score":0.7198603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022735384683664,"score_gpt":0.3213407420082993,"score_spread":0.2190672035399329,"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."}}