{"id":"W4401660162","doi":"10.32920/26767801","title":"Development of the Food Label Information Program: A Comprehensive Canadian Branded Food Composition Database","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food composition data; Pace; Food supply; Business; Composition (language); Database; Food science; Marketing; Agricultural science; Computer science; Geography; Chemistry; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004350093,0.001161802,0.0008909727,0.01212116,0.002769242,0.003988412,0.004144331,0.0006940723,0.01322301],"category_scores_gemma":[0.01428438,0.0008909807,0.0009143039,0.0165238,0.0006056582,0.003111819,0.002556869,0.001645848,0.008489517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02290137,"about_ca_system_score_gemma":0.07941194,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9204611,"about_ca_topic_score_gemma":0.9224409,"domain_scores_codex":[0.995408,0.0002714178,0.0003196043,0.000558815,0.003095793,0.0003464356],"domain_scores_gemma":[0.9757341,0.00100249,0.0006355313,0.001666279,0.01913964,0.001821982],"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":[0.0004536451,0.0002557895,0.02607136,0.0009591855,0.0001315338,0.0002231908,0.0009542121,0.00415163,0.004165118,0.0107497,0.6585663,0.2933184],"study_design_scores_gemma":[0.000186754,0.0000892856,0.05661451,0.0003611358,0.0001392646,0.00009247923,0.001337098,0.02240694,0.009364363,0.002923964,0.9062466,0.0002376424],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01871978,0.0006023396,0.0651722,0.00154566,0.0001955307,0.004393134,0.8493198,0.01541162,0.04463997],"genre_scores_gemma":[0.02910322,0.000619981,0.1473736,0.0004152293,0.00003978049,0.001995643,0.8067119,0.001125562,0.01261498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07953894,"threshold_uncertainty_score":0.1661618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04938334485826896,"score_gpt":0.2950504391451699,"score_spread":0.245667094286901,"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."}}