{"id":"W4210700814","doi":"10.3389/fnut.2021.825050","title":"Development of the Food Label Information Program: A Comprehensive Canadian Branded Food Composition Database","year":2022,"lang":"en","type":"article","venue":"Frontiers in Nutrition","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; University of Ontario Institute of Technology","keywords":"Food composition data; Added sugar; Business; Database; Pace; Food processing; Food science; Marketing; Computer science; Sugar; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004788293,0.001201815,0.000840231,0.01107509,0.002496965,0.003837419,0.004359436,0.0006349128,0.009750191],"category_scores_gemma":[0.01412793,0.0009168914,0.0008863363,0.01522069,0.0005921349,0.003270048,0.002503326,0.001533448,0.005856772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02023003,"about_ca_system_score_gemma":0.07250571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8922989,"about_ca_topic_score_gemma":0.8803166,"domain_scores_codex":[0.9954853,0.000290169,0.0003498391,0.0005685233,0.002943279,0.0003629521],"domain_scores_gemma":[0.975573,0.001102239,0.0007845085,0.001767881,0.01908136,0.001690914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007696567,0.000384967,0.05180205,0.001355967,0.000198503,0.0003229835,0.001269861,0.007217528,0.006547543,0.01192239,0.5436389,0.3745697],"study_design_scores_gemma":[0.0002785746,0.0001632977,0.08888236,0.0004775036,0.0002022844,0.0001542822,0.001407907,0.04069708,0.01459692,0.003522393,0.8492935,0.0003238716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03190012,0.0007456627,0.09328864,0.001597544,0.0001727429,0.005374202,0.8059029,0.02291774,0.03810047],"genre_scores_gemma":[0.04491154,0.0006268941,0.1894615,0.0003574726,0.00004468324,0.002122767,0.7517169,0.001245588,0.009512773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1077011,"threshold_uncertainty_score":0.2166708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02218445528722832,"score_gpt":0.2472816771664633,"score_spread":0.2250972218792349,"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."}}