{"id":"W2745318707","doi":"10.1016/j.jand.2017.06.068","title":"Improving Sandwich Selection Can Favorably Impact Nutrients to Limit in Children: A Modeling Analysis Using What We Eat in America, 2003-2014 and the USDA Typical Food Pattern","year":2017,"lang":"en","type":"article","venue":"Journal of the Academy of Nutrition and Dietetics","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"Selection (genetic algorithm); Limit (mathematics); Nutrient; Environmental science; Environmental health; Computer science; Mathematics; Biology; Medicine; Ecology; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005050908,0.00112172,0.0009440522,0.0004895399,0.0007679456,0.001543014,0.002341122,0.001758654,0.00503935],"category_scores_gemma":[0.006363078,0.0007744498,0.005151213,0.0007568572,0.0007532506,0.0009042391,0.001094204,0.002258708,0.0005457891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001636982,"about_ca_system_score_gemma":0.002149441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1576049,"about_ca_topic_score_gemma":0.1168871,"domain_scores_codex":[0.9983485,0.0009423428,0.0000544112,0.0002570573,0.00005879048,0.0003389895],"domain_scores_gemma":[0.9947807,0.003738034,0.0005802105,0.0002866338,0.000198067,0.0004163508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003102216,0.001301542,0.9126242,0.00008056391,0.007544166,0.00031196,0.0006031559,0.06344599,0.0007087065,0.001453628,0.002017353,0.006806578],"study_design_scores_gemma":[0.0006670678,0.002916596,0.6706934,0.0001137129,0.01203176,0.0002420937,0.00326605,0.3021455,0.001229329,0.00232577,0.004257209,0.0001114353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978412,0.0002540409,0.0007011061,0.0003848078,0.00001983847,0.00001298452,0.0004683864,0.00002249496,0.000294985],"genre_scores_gemma":[0.9970813,0.0001119352,0.00074863,0.0001004267,0.0000113378,0.00002965529,0.0005328975,0.00001879322,0.001364989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1576049,"threshold_uncertainty_score":0.313375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110431335559548,"score_gpt":0.4257959171878888,"score_spread":0.3153645816283407,"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."}}