{"id":"W3049040973","doi":"10.3390/foods9081127","title":"Methodology for the Determination of Fruit, Vegetable, Nut and Legume Points for Food Supplies without Quantitative Ingredient Declarations and Its Application to a Large Canadian Packaged Food and Beverage Database","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Consumer Attitudes and Food Labeling","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Toronto","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Mitacs; University of Toronto","keywords":"Ingredient; Nut; Legume; Recipe; Food science; Database; Food products; Mathematics; Chemistry; Computer science; Engineering; Biology; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004395493,0.0001120059,0.0002455562,0.00008626587,0.0002044782,0.00001973425,0.00004734977,0.00005535718,0.000002372661],"category_scores_gemma":[0.0006616425,0.00009211301,0.00002577322,0.000113482,0.00003686152,0.00008339861,0.00004904861,0.00007535445,2.75408e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001767987,"about_ca_system_score_gemma":0.00008691639,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002297139,"about_ca_topic_score_gemma":0.04422367,"domain_scores_codex":[0.9992269,0.00004479791,0.0002008457,0.0002648613,0.00007217565,0.0001904781],"domain_scores_gemma":[0.9989302,0.0005381684,0.00007279723,0.0001207412,0.000145511,0.0001926026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006257459,0.0006130353,0.1093482,0.008841952,0.004475544,0.000007626292,0.1134513,0.00003897508,0.3738198,0.2664192,0.0008317173,0.1158952],"study_design_scores_gemma":[0.04182269,0.04749781,0.2047327,0.001104321,0.009184683,0.0002072986,0.0169489,0.3664712,0.1878136,0.01024696,0.1114161,0.002553694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5277106,0.004837155,0.4558035,0.006584363,0.00005047277,0.003285887,0.001690423,0.00001802578,0.00001959641],"genre_scores_gemma":[0.942168,0.00005977283,0.05663684,0.0006103329,0.00003332069,0.0003652271,0.0001039377,0.00001586728,0.000006714919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4144574,"threshold_uncertainty_score":0.9732168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1046333114638755,"score_gpt":0.3585482321519493,"score_spread":0.2539149206880739,"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."}}