{"id":"W3015319084","doi":"10.1016/b978-0-08-100596-5.22772-8","title":"Food Constituent and Food Metabolite Databases","year":2020,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions; Genome Canada","keywords":"Food composition data; Metabolite; Composition (language); Database; Food processing; Food science; Computer science; Chemistry; Biochemistry","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":[0.0007327544,0.001672595,0.00102755,0.007403539,0.0004971104,0.003309027,0.001562972,0.001097637,0.1167349],"category_scores_gemma":[0.001370479,0.0006980344,0.0006940716,0.009467491,0.0002605839,0.002824385,0.001794803,0.0007522233,0.13703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005462863,"about_ca_system_score_gemma":0.001179176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400576,"about_ca_topic_score_gemma":0.004393398,"domain_scores_codex":[0.9997017,0.00002205157,0.00003786367,0.00008702903,0.0001300036,0.000021281],"domain_scores_gemma":[0.9995545,0.0001106534,0.00005119821,0.0001300882,0.00009810965,0.00005544491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003286242,0.00008194464,0.0009350784,0.001855339,0.00006929473,0.0002991699,0.000084546,0.001057702,0.01710342,0.007855237,0.3560235,0.6143063],"study_design_scores_gemma":[0.00002655037,0.00002170877,0.001932614,0.0002736751,0.00003502624,0.000311576,0.00006226055,0.001497577,0.005962819,0.00671294,0.9831384,0.0000248319],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.007165837,0.01321188,0.06486711,0.001396571,0.0004413389,0.0002420629,0.6609697,0.04155209,0.2101534],"genre_scores_gemma":[0.009847146,0.01226169,0.06596725,0.0005962926,0.0001527121,0.0001698296,0.7963508,0.003486932,0.1111673],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1167349,"threshold_uncertainty_score":0.3905172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02301855830284831,"score_gpt":0.2379526388971387,"score_spread":0.2149340805942904,"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."}}