{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02871225,0.001588249,0.001060275,0.01621842,0.002269632,0.00305501,0.003696244,0.0007970963,0.007749162],"category_scores_gemma":[0.05353662,0.0009885795,0.001569961,0.02312498,0.001170874,0.001248614,0.003375161,0.001547964,0.002794996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008266092,"about_ca_system_score_gemma":0.02447082,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3940288,"about_ca_topic_score_gemma":0.5064901,"domain_scores_codex":[0.9673318,0.005806321,0.004628534,0.003868105,0.01749817,0.000867144],"domain_scores_gemma":[0.9666625,0.006288977,0.005912076,0.004586346,0.01610502,0.0004451298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006190903,0.0007558404,0.1929853,0.001887481,0.0006696759,0.0003079124,0.004434162,0.008249024,0.02223651,0.01931291,0.03641685,0.7121254],"study_design_scores_gemma":[0.0004317945,0.0008263115,0.5653864,0.0005466588,0.0003986371,0.0008555158,0.004199351,0.1387555,0.05148894,0.009948607,0.2264312,0.0007310772],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04302042,0.0005342542,0.8942652,0.000338449,0.0001217236,0.01692671,0.03176688,0.004101571,0.008924733],"genre_scores_gemma":[0.03472348,0.0002028629,0.9411027,0.00009926666,0.00002008973,0.009761726,0.01127634,0.0002233313,0.002590195],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6059712,"threshold_uncertainty_score":0.7834706,"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."}}