{"id":"W4282835807","doi":"10.1093/cdn/nzac077.021","title":"Automation of the Updated Food Label Information Program (FLIP 2020): A Comprehensive Canadian Branded Grocery and Restaurant Food Composition Database","year":2022,"lang":"en","type":"article","venue":"Current Developments in Nutrition","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Food composition data; Database; Product (mathematics); Composition (language); Marketing; Food science; Advertising; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002563958,0.00183945,0.001045368,0.009449108,0.002112187,0.0036184,0.003154116,0.0006747427,0.009517216],"category_scores_gemma":[0.007966704,0.0009338537,0.000893289,0.009392514,0.0006623341,0.002688682,0.002929764,0.001298496,0.006653471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01108332,"about_ca_system_score_gemma":0.03036911,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8626241,"about_ca_topic_score_gemma":0.8311062,"domain_scores_codex":[0.9959381,0.0001764557,0.0001987179,0.0006662779,0.002587242,0.0004331573],"domain_scores_gemma":[0.988176,0.0006394378,0.0003336554,0.001198072,0.008875234,0.0007776312],"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":[0.001042354,0.000369239,0.03221688,0.0007868031,0.0001793998,0.0004965079,0.001271783,0.006176045,0.01633672,0.006364836,0.5852557,0.3495038],"study_design_scores_gemma":[0.0003729965,0.0001823852,0.1156256,0.0003685793,0.0002028409,0.0003394427,0.001616061,0.1150906,0.05555573,0.003313712,0.7068824,0.0004496215],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07900227,0.001086538,0.1092233,0.00118527,0.000312873,0.00318744,0.6434246,0.09948429,0.06309349],"genre_scores_gemma":[0.1015649,0.0006299204,0.1814308,0.0003863018,0.00005447404,0.001080045,0.6972701,0.00341638,0.0141671],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1373759,"threshold_uncertainty_score":0.2763698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02699498052428487,"score_gpt":0.2815384680489415,"score_spread":0.2545434875246566,"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."}}