{"id":"W2128655219","doi":"10.1111/j.1753-4887.2007.00003.x","title":"Nutrient profiling of foods: creating a nutrient-rich food index","year":2008,"lang":"en","type":"review","venue":"Nutrition Reviews","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"Food and Nutrition Service","keywords":"Nutrient; Nutrient density; Profiling (computer programming); Biotechnology; Environmental health; Environmental science; Business; Computer science; Biology; Medicine; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005312773,0.0008076217,0.005789001,0.0004727315,0.0002438235,0.00002162205,0.0002486243,0.0005124374,0.00009415001],"category_scores_gemma":[0.0007203622,0.0006269267,0.00213625,0.001372604,0.0001555518,0.0000890151,0.0001465326,0.0007274519,0.0001215606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004033206,"about_ca_system_score_gemma":0.0003248818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005455659,"about_ca_topic_score_gemma":0.000001380366,"domain_scores_codex":[0.9946474,0.0003180997,0.002768309,0.0008173412,0.000907787,0.0005410848],"domain_scores_gemma":[0.9966127,0.0002438773,0.00161751,0.0005897977,0.000671106,0.0002650243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001406134,0.006062579,0.0001792245,0.5864654,0.0009684222,0.00006548826,0.00007328439,5.946966e-8,0.00000520427,0.004583422,0.1071864,0.2942699],"study_design_scores_gemma":[0.001828754,0.0007256759,0.000004333254,0.1295796,0.0008757905,0.0004102941,0.00004809556,0.000001000623,0.0000243283,0.0003834996,0.8656986,0.0004200068],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001150657,0.9867299,0.0001532612,0.00008799852,0.0002112314,0.008052829,0.0003096388,0.00008086529,0.004362765],"genre_scores_gemma":[0.00001353328,0.9898514,0.004462407,0.0001800139,0.001152383,0.003219617,0.0008859321,0.00009692567,0.0001378235],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7585123,"threshold_uncertainty_score":0.9996182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09820609392382475,"score_gpt":0.3625595744491562,"score_spread":0.2643534805253315,"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."}}